Search

The success of drilling new horizontal wells depends on connecting the well’s surface location with the targeted formation while achieving the desired lateral length. However, the choice of the surface locations can be limited due to difficulties obtaining the permits to drill on these locations. This creates more deviated wellbore designs and decreases the optimized lateral length. To avoid this, a risk matrix will be developed to evaluate the maximum lateral length for different surface locations. The first step to creating the risk matrix is to derive a geometry-focused torque and drag model. This model will utilize the wellbore’s step-out and geometrical torsion, which are resulting of both a deviated well design and a far surface location. In this paper, the step-out for any given well path design will be considered as the sum of the horizontal projections of all the deviated segments of the well trajectory above the last curve’s KOP. The calculation of the step-out will be incorporated into the model, then will be used to calculate the maximum lateral length versus the step-out. A torque and drag model with the simplicity of the soft-string model was created while taking into consideration the wellbore’s geometrical torsion to capture the effect of the well’s trajectory on the axial forces and the torque. This effect will partially simulate the deformation of the drill pipe using simplified mathematical expressions comparing to the calculations of the stiff-string model. This methodology resulted in the model to be more sensitive to the axial force than the torque. The built risk matrix uses the derived model to highlight one of two mechanisms that determine the dominant constraint that limits the drilling process: either reaching the maximum hook load or the maximum torque, based on the wellbore design. These limits depend on the rig’s capacity and the drilling company. The risk matrix summarizes multiple designs to conveniently compare between different step-out values and their respective maximum later lengths. The risk assessment is quantitative rather than qualitative and is reflected as the percentage used of the dominant constraint for the given designs and the interval of the lateral lengths at which this percentage is reached. The optimum surface location with its step-out is then chosen easily from the created risk matrix. The built model takes a middle position between the soft-string model and the stiff-string model by capturing the geometrical torsion along the wellbore trajectory. This approach enhances the geometrical optimization when calculating the axial force and the torque. The simplified mathematical derivation incorporates the step-out into the model, thus creating the risk matrix to optimize the lateral length based on the surface location.

The project ahead wellpath trajectory models are built on assumptions of static earth model and wellpath error models which may not be valid all the time. But the data will provide additional support to suppress the assumption and improve the models. So, an optimized project ahead model that combines geometry, bottomhole assembly (BHA) dynamics and dynamic earth model is needed for a practical project ahead prediction. This paper presents a coupled model with embedded uncertainty not only for projecting ahead the path but also steering to achieve the path. This paper presents the results that can be used under uncertainty through planning, through realtime project ahead, most importantly by integrating all the above four models. Planning and executions are two primary approaches to intelligent decision making at the edge when the well is constructed. Execution enables us to take immediate actions far into the future, but it requires accurate well trajectory models, and the current and past realtime data. When it comes to the edge decision whether it is manual or semi-automated or fully automated it requires tighter coupling of engineering models through microservices and reinforced learning from the data and feedback from the driller. Four major types of models are considered in this paper to arrive at the dynamic path as the well is drilled. 1. Soft landing path based on dogleg as constraint 2. Minimum well profile energy that can be achieved 3. Mechanical drillahead model based on the steering tool 4. Productivity coupled earth model for various input parameters from earth model to operational parameters during drilling.

The objective of this study is to monitor and forecast how well the casing string can be run during the drilling of the well's particular hole section. The runnability of the casing is measured by the use of a comprehensive index supported by real-time monitoring of multiple features with estimated indicators. The methodology developed by combining geometrical and mechanical calculations provides an assessment of whether the casing can be run without a short landing. This provides more opportunities for adjustments that can be made during drilling itself. In general, the "smoothness" that connects the wellbore geometry and mechanical system is related to the borehole quality utilized to run the casing. With real-time drilling parameters and complex equations represented with common sense, it is possible to assess the runnability of the casing for a given hole size. When taken together, wellbore tortuosity, borehole torsion, wellprofile energy, well curvature passage through force, margin of slack off force and overpull margin support the evaluation of a proposed wellpath to determine the casing's runnability. Combining operational experience and trajectory metrics in a methodology has proven to be effective in assessing operational risk for tasks like running casing in curve construction sections or to total depth on prolonged laterals. In addition, this paper discusses how the runnability metrics can help to anticipate or diagnose other challenges such as complex stimulation work, artificial lift performance and possible well servicing complications that may arise because of unwanted curvature, borehole torsion and wellbore drift. Presently there is no method to predict the runnability of casing to bottom while the well is being drilled. A new and practical method of quantifying the runnability of casing during drilling has been analyzed and proposed in this paper.

The oil and gas industry is transforming itself into a data-intensive industry with the addition of artificial engineering intelligence, cognitive computing, machine learning, internet of things and robotics. With the growth and expansion, various exponential technologies can carry the energy industry's transformation forward in the new era of cyber-automation and more so on the cyber-physical -automation. Blockchain technology can drastically streamline the current complex matrix of activities from seismic to production and abonnement. The existing integration and management of various disciplines is very expensive, requires several disciplines that are sometimes disjointed and siloed, and is exceedingly complex in the course of the digital well program and as we receive more and more data. With blockchain as a common technology, firms may more easily handle the many digital procedures, operational tasks, diverse vendors, production schedules, and supply chains that affect how the digital well program and schedules are made. In this paper, the full lifecycle system—which can serve as the lifecycle's template—is discussed. It can be housed on a permissioned blockchain. Blockchain has been discovered to streamline and facilitate open collaboration because to its inherent qualities of trustworthiness and transparent execution of transactions involving numerous parties. By making asset transactions transparent and flexible, it facilitates data sharing among several businesses, providers, and partners, making it simple to connect the real world to the digital one through the use of edge devices and cloud computing. This approach has been discovered to offer crucial characteristics of privacy and auditability while limiting ledger size, in addition to transparency and efficiency. In order to balance the competing demands of privacy and openness, peer identities are concealed from one another while they agree on transaction outcomes. Additionally, data will never be exchanged among blockchain peers without the owners' express permission. Blockchain is a decentralized peer-to-peer digital ledger technology that has a lot of promise for the digital well program. A local peer communicates with an associated anchor peer inside the global network and a common database like Open Subsurface Data Universe in the system, which is built for both intra- and inter-organizational transactions like Open Subsurface Data Universe (OSDU).

Over the years, some researchers have used Mechanical Specific Energy (MSE), which is said to represent the amount of energy needed to drill a unit volume of rock, to quantify drilling efficiency. MSE was originally introduced by Teal for the mining industry in 1964. Since then, MSE has taken different forms for other reasons, based on its interpretation, and intended use. This paper provides a comprehensive review of MSE in general, discusses its different forms and narratives, and draws the readers' attention to common (and not so common) facts, pitfalls, and fallacies, of using MSE.It has been found that these specific energy concepts are held as true for all predictive purposes in drilling, amended and promoted beyond the original framework. The paper analyzes all the equations presented in the past and quantifies each component of the equations. The hydraulic terms used alongside the mechanical terms are also discussed. Extensive simulations have been carried out and will be reviewed in this paper by quantifying the energy under each term based on rate of penetration effects and implications. We aimed to demonstrate in this paper, the theoretical grounds for pitfalls and fallacies in using MSE.MSE is made up of two components: torsional energy and thrust energy. The results have shown that the thrust term is much smaller than the second torsional energy term and in most of the cases, about 2% or less. Hence, it could be neglected and thereby the equation results in the form of inverse of the rate of penetration (ROP) making the calculated MSE value redundant when the actual ROP is available. The results also have shown that when the hydraulic energy term is subtracted from the MSE equation, it results in negative rate of penetration and thereby shows a fundamental flaw in the system formulation. The purposed and merits of MSE use, by some researchers to identify drilling dysfunctions, will also be highlighted. In this process, it has been shown that nonlinear "torque wedging" causes inaccuracies in dysfunctions identification and discussions. Also, the field data presented in the paper shows that mechanical energy is not a ratio of input energy and rate of penetration. Moreover, none of the studies have accounted accurately for the effects of bit wear and motor wear on MSE. It has been found that overall, the concept relating to dysfunction quantification is a self-destructive process, which has spread from paper to paper without the required checks and verifications for accuracy. The underpinning discussions have been backed and demonstrated with numerical examples.The paper provides the pitfalls in the omissions of some of the assumptions in various MSE models used by engineers. This helps the users to carefully plan, design, engineer and construct the wells.

Different methods to calculate torque and drag have been used by the oil industry for years. Various assumptions are used in formulating these models. Opinions have often varied as to which model is better and whether the assumptions are valid. This paper discusses the trilemma considerations for an engineer 1. Soft string model vs stiff string model 2. Survey calculation method based on minimum curvature 3. Appearing and vanishing tortuosities after the casings have been run.Because of various underlying assumptions, the friction factor calibration is done often to match the calculated results with the actual results. This results in a wrong conclusion due to the change in the drag forces but on the contrary, it may be due to the exacerbation of the assumptions. The paper presents the pitfalls of the drillstring models, borehole curvature, appearing and vanishing tortuosities and their relationship with related well engineering calculations. Mathematical underpinnings are provided for all the trilemma considerations.Results demonstrate that a string with a large-size section can be very soft in a straight wellbore, and a string with small-size section can be very stiff in a wellbore with severe tortuosity, which is better fit for a stiff string model. Results also confirm that the soft string model is a better choice when the string is slimmer, the wellbore is in a lower curvature shape, and the clearance is larger. It has been observed that the absence or the discontinuity of bending moment results in the underestimate of forces, torque, and stresses when the minimum curvature method is used. This vanishing tortuosity alters the apparent wellpath and the new tortuosity representative of the cased hole path may present new appearing tortuosity and results in over and under estimation of the torque and drag calculations. To accurately estimate the drag force, the stiffness, as well as the wellbore shape and its clearance, should be considered.

Surface hookload and torque values serve as good indicators for some undesirable scenarios or anomalies during drilling, such as stuck pipe, buckling, and inadequate hole cleaning. However, to detect these risks, it requires drilling engineers to perform the friction factor calibration manually and regularly, which costs more effort and poses significant uncertainties on the detection. In this paper, a cloud-based real-time well engineering webapp has been developed to monitor and forecast tripping frictions and drilling performance. Results of field tests were presented to prove the successful testing of this cloud- based real-time workflow. Real-time hookload and torque values were streamed smoothly to the web application interface. Rig activity, friction factor, and mechanical specific energy (MSE) were also evaluated and displayed in real-time with predicted uncertainty zone. It has been demonstrated that this cloud-based web application supports a multi-tenancy architecture and multiple wells can stream simultaneously with no down time. This new workflow made it possible for drilling engineers to monitor live drilling wells anywhere and anytime while enabling the rig personnel to make significant improvements to operations and make timely and accurate decisions.

Vibration signals in the form of real-time accelerations recorded downhole often contain strong noise, making it difficult for fault or failure diagnosis during drillstring. Vibration signals include noise from sources, such as motors, bit and drillstring interactions with the borehole, rugged boreholes, and similar interactions. Sometimes, this noise is stronger than the underlying signal, which might lead to false alarms or misrecognition. This paper discusses a novel approach to diagnose failure in real-time, which is also robust to noise.Existing methods for fault or failure diagnosis are based on threshold values of peak and average vibrational signals. This paper introduces a hybrid method of combining signal demodulation with spectral analysis to predict drillstring failure. This method deconvolutes the signal with the help of minimum entropy deconvolution (MED) and Teager-Kaiser energy operator (TKEO) to remove ambiguity as a result of noise. Then, the signal is decomposed into various intrinsic mode functions (IMFs) that have the highest correlation with the original signal and can be used for failure diagnosis.This paper also discusses how spectral analysis can be applied on selected IMFs by comparing the IMF’s impact frequency with the system’s natural frequency so its harmonic drillstring failure can be diagnosed more precisely.

Planning and learning are two primary approaches to intelligent decision making at the edge when the well is constructed. Planning enables us to take immediate actions far into the future, but it requires accurate well engineering models, which are often difficult to acquire in practice. The models are built on engineering assumptions which may not be valid all the time. But the data will provide additional support to suppress the assumption and improve the models. This paper presents the results that can be used under uncertainty through planning, through learning, most importantly by integrating planning and learning. Planning and learning are two primary approaches to intelligent decisions. When it comes to the edge decision whether it is manual or semi-automated or fully automated (Dupre, 2013) it requires tighter coupling of engineering models through microservices and reinforced learning from the data and feedback from the driller. Five major types of uncertainties are considered in calculating the drilling operational parameters are measurement uncertainty, data uncertainty, engineering model uncertainty, computational or algorithmic uncertainty and decision uncertainty. Several examples are presented as the well is steered and navigated with interactive tasks. Surface hookload and torque values serve as good indicators for some undesirable scenarios or anomalies during drilling, such as stuck pipe, buckling, and inadequate hole cleaning. However, to detect these risks, it requires drilling engineers to perform engineering model calibration manually and regularly, which costs more efforts and poses significant uncertainties on the detection. This paper describes how these problems are circumvented in addition by providing project ahead paths based on various constraints in real-time with predicted uncertainty zone. This option at the edge makes it possible for drilling engineers to monitor live drilling wells anywhere and anytime while enabling the rig personnel to make significant improvement to operations. Hence, the optimization is no longer static and becomes a dynamic function of depth. These conditions result in constantly varying constraints and, thereby, constantly varying optimized operating parameters to maximize the rate of penetration or minimize specific energy or well cost. This study presents a real-time optimization technique for rate of penetration with energy-based models. Based on the place and position of the bit, the updated data has been used to modify the proposed well design on the fly. The above underpinning methodology have been supported with practical field examples.

As we develop autonomous drilling system the setpoint operational and control, parameters for drilling are to be determined. The decision-making control loop performance should be faster. As the system with high aspect ratio i.e., length over diameter is highly stochastic, calculating the parameters for the optimal condition is very difficult. The paper presents methodology to obtain the envelop of sufficed, satisfied, and optimized engineering conditions resulting in "optosatisficed" condition aka "artificial engineering intelligence" which is good enough or an acceptable solution or a reasonable solution under the given condition. The paper presents an adaptive model which connects different engineering models in the form of microservices for constraints rather than a complete theoretical optimal closed form solution-based model. The approach to be taken under this condition is to include the stability of the bit, drillstring and wellbore, which will result in the near optimum envelop (desired) for the drilling parameters. Important analyses are coupled - vibration-stability analysis and bit-wear performance analysis. The optimum rate of penetration limiting values are calculated as the well is drilled with specified interval by considering the bit wear and the vibration. At each drilled interval, it is assumed that the bit wears out continuously and the amount of wear depends on the uncontrolled and uncertain parameters such as formation, anisotropy, dip etc. Also, all the calculations such as hole cleaning mechanical specific energy are calculated continuously. The analysis results have shown that the convergence was very quick in obtaining optimal solution and the predictability in the test wells have shown best solution results under uncertainty. Also, it has been found that the results provide reasonable threshold values when more and more data are used as the well is drilled. The stability envelop is shown in the figure with multiple connected engineering microservices. As long as the driller stays within the operational region, the results have shown that the operating parameters are satisfying and good enough solution for the desirable outcome. In other words, near normal engineering solution is achieved. Updating of the stability plot at every discrete drilled interval provides the pattern of the envelop for further decision-making set points for the controller in the case of an autonomous system. This paper provides a new framework for faster decision strategy. Injecting life to the well design software itself will provide amazing power. When a well is designed or monitored or drilled, well plan can be made interactive so that user can optimize and take proper decision to drill ahead or automate the operation.

In geothermal wells, temperature effect plays a very important role because of its high and cyclic temperature operation, usually in the range of 150 °C to 450 °C. Casing designed for traditional oil and gas production cannot be simply used in geothermal wells because of their different working conditions in these two scenarios. Thus, a robust design methodology for the geothermal casing is important from both economic as well as safety perspective. Various studies have reported that the single most common failure in high-temperature geothermal wells is the mechanical overload of casing string due to constrained thermal expansion. In this research work, the classical Holliday approach for the post-yield design is reviewed and modified to include the effects of thermal yield strength deration effect and Bauschinger effect in the design process of high temperature cyclic loading encountered in case of geothermal wells scenarios. In this study, the influence of thermal deration of yield and the Bauschinger effect on the allowable stress window is investigated. Empirical relations are used to estimate the reduction in the allowable stress window. In this research work, it has been found that the thermal derations effect and Bauschinger effect cause significant reduction in the allowable stress range. A reduction of 30% in the allowable stress window is observed due to these two factors from a case study. So, these two effects should be properly considered when doing a casing design for geothermal well. Holliday approach ignored several effects due to the high temperature cyclic loading occurring in case of geothermal wells. This research work has addressed the limitations of the Holliday's classical approach to include these effects to make the design more robust for the geothermal well tubular design.

The lithology of the formation is known to affect the drilling operation. Litho-facies help in the quantification of the formation properties, which optimizes the drilling parameters. The proposed work uses the artificial neural network algorithm and an optimizer to develop a working model for predicting the lithology of any formation within the study area in real-time. The proposed model is trained using the formation data comprising 15-dependent variables from the Eagleford region of the United States of America. It builds a method for measuring or forecasting litho-facies in real-time when drilling through a formation. It uses general drilling parameters for better precision, including Rate of Penetration, Rotation per minute, Surface Torque, Differential Pressure, Gamma Ray Correlation, and a d-exponent correlation function. The proposed model compares and assesses various first-order optimization algorithm's efficiency, such as Adaptive Moment Estimation, Adaptive Gradient, Root Mean Square Propagation, and Stochastic Gradient Descent with traditional artificial neural network in quantitative litho-facies detection. The model can predict the complex lithology for vertical/inclined/horizontal wellbores in real-time, making it a novel algorithm in the industry. The developed algorithm illustrates an accuracy of 86 % using Adam optimizer when tested with the existing data and improves as the model is trained with more data.

SummaryDrilling technology in petroleum engineering is associated with problems such as high fluid loss, poor hole cleaning, and pipe sticking. Improvement of rheological and filtration properties of water-based drilling fluids (WDFs) plays a major role in resolving these drilling problems. The application of nanotechnology to WDF in the recent past has attracted much attention in addressing these drilling operations problems. In the present work, we investigate the application of natural aloe vera and CuO nanofluids combined as an additive in WDF to address the drilling problems. The nanofluids of three different concentrations of CuO nanoparticle (0.2, 0.4 , and 0.6 wt%) with aloe vera as a base fluid are prepared for this study by adopting a two-step method. The prepared nanofluids are characterized by their particle size and morphological characteristics. Conventional WDF (DF.0) is synthesized, and the prepared aloe-vera-based CuO nanofluid is added to the WDF to prepare nanofluid-enhanced water-based drilling fluid (NFWDF) of different concentrations of nanoparticles, namely, 0.2 , 0.4, and 0.6 wt%. The prepared drilling fluid mixture is then characterized for its rheological and filtrate loss properties at various temperatures. Thermal stability and aging studies are performed for both WDF and NFWDF. The experimental results are then modeled using rheological models. The results reveal that aloe-vera-based CuO nanofluids improve the thermal stability and rheological properties of drilling fluid and significantly decrease the American Petroleum Institute (API) filtrate. Viscosity for WDF shows an approximately 61.7% decrease in heating up to 90°C. Further, the hot roll aging test causes a 63% decrease in the viscosity of WDF at 90°C. However, the addition of aloe-vera-based CuO nanofluids is found to aid in recovering the viscosities to a great extent. The fluid loss values before hot rolling are observed to be 6.6 mL after 30 minutes, whereas fluid loss values for the NFWDFs are found to be 5.9, 5.4, and 4.6 mL, respectively. The fluid loss value after hot rolling for the WDF is found to be 10.8 mL after 30 minutes, whereas fluid loss values for the NFWDFs are found to be 9.2, 8.5, and 7.7 mL, respectively. The rheological performance data of NFWDF project a better fit with the Herschel-Bulkley model and suggest improvement in rheological and filtration properties. There has been limited research work available in understanding the impact of aloe-vera-gel-based nanofluids in improving the performance of WDFs through the improvement of its rheological and filtration properties. This study aims to exploit the property of native aloe vera and CuO nanofluids combined together to enhance the rheological and filtration properties of WDF by conducting the tests both before and after hot rolling conditions. This study acts as an important precursor for developing novel additives for WDF to improve its rheological and filtration properties. This study is also expected to benefit the industry and solve the major challenges in deep-well drilling operations and high-pressure and high-temperature (HPHT) drilling operations.

The increasing complexities of wellbore geometry imply an increasing potential of damage resulting from downhole casing wear. The present method of using old wear factors results in unreliable predictions. The main objective of the present work is to develop new casing wear factors for drillpipes and casings to improve the accuracy of wear prediction. The paper focuses on design, manufacturing, control and measurements of important parameters contributing to casing wear as well as wear volume evaluation. A new casing wear testing facility is designed and built by repurposing and automating an old lathe machine. Real drillpipes with tool joints and casing sections made of different materials are used. The lathe spindle controls the mandrill rotational speed on which the DP is tightly mounted. The versatile casing section holder is fixed on a sliding system comprising a dynamometer and a step-motor with a microcontroller allowing for the control and measurement of the side loadings and lateral displacements. A slurry fluid pump system is designed to feed water-based or oil-based muds at the contact surfaces. The as-received drillpipes and casing materials were analyzed by microscope and optical emission spectrometry (OES) to determine their microstructure and chemical composition. The hardness of both casing and DP materials was also measured. The lathe main spindle was able to provide stable rotating speeds varying from 100 to 1000 rpm. Though the irregular tool joint hard-facing surface was machined to allow for better control of the side loadings, the measured radial load shows variations of +/− 15%. The designed pumping system was able to provide a continuous stream of water-based and oil-based mud, at the contact surface, during testing. The wear depth is measured both directly using a 3D optical profilometer and indirectly by the lateral sliding displacement, thus allowing for the establishment of a calibration curve that can be used for in-situ measurement of the wear depth and wear volume evaluation. The initial set of tests has shown that the designed system is performing satisfactorily. More tests are being performed to confirm the robustness of the design.

In recent years multiple papers on borehole torsion, well curvature, and well profile energy have been published, highlighting the potential value in describing the well path with these new indices. (Samuel et al. 2021; Samuel and Zhang 2019; Samuel and Liu 2009; Samuel et al. 2005) In practice, these well path calculations have proven more challenging to calculate in comparison to more common measures such as dogleg severity. To enable the use of these new metrics, a web application was developed to make the outputs accessible to drilling engineers globally. Once available, the use of torsion and curvature aids in interpreting and anticipating downhole trajectory related issues and assists in differentiating between borehole cleaning and trajectory problems. This paper presents an overview of the web application, the method used for calculation, and two case studies demonstrating the real time application of torsion and torsion/curvature ratio representing how they provide additional insight beyond what is available from evaluating dogleg severity alone. A real-time methodology has been implemented by combining operational experience with multiple trajectory metrics. Proactive wellpath evaluation proved to be key in identifying measured depth intervals with higher likelihood for operational challenges during casing running and tripping operations in Permian Delaware Basin laterals.

Automating drilling operations using modern technology with intelligent control systems is prioritized with respect to drilling process development. The process becomes more complex when a mud motor is used because of the dynamic properties of the control objects, which are interconnected from the surface feed mechanism to the bit. Development of a model that better predicts the dynamic condition when a mud motor is used is discussed. The dynamic characteristics of a positive displacement motor (PDM) are considered as a single system by coupling the hydromechanical processes. This includes, transient pressure, drillstring dynamics, kinematics of the PDM rotor, the mud pump and dampener, dynamic characteristics of the bit, and internal and external system disturbances. The mathematical model includes the hydraulic and mechanical subsystems as well as the relationships of these subsystems. The subsystems include the equations of the processes in their constituent links (i.e., drillstring, hydraulic line, mud pump, downhole motor, bit, and bit feed mechanism). The nonlinear system of differential equations are solved using numerical methods with appropriate boundary conditions in a two-way loop for regulating the load on the bit and flowrate. The study shows that the transient behavior of the mud motor must be accounted for when automating the drilling process. It has been observed that an instantaneous change in the load results in the transition of the hydraulic motor to a new steady-state mode gradually (for tens of seconds) and is influenced by the transient pressures in the string. The transient process (torsional vibrations) in the PDM occurs until the flow rate stabilizes at the top of motor. A similar hydromechanical effect, if the PDM does not have a sufficient torque reserve, can lead to deceleration of the PDM and might require correction of the weight on bit (WOB) and flow rate. The study also showed that the effect of pulsation dampener resulting in an uneven flow leads to uneven rotation of the mud motor shaft, even if a constant load on the bit is maintained. This study revealed that evaluation of the influence of the deviation of one of the operating parameters on the behavior of the dynamic system is important (Tokhonov, 2019). The choice of optimal control algorithms depends on the accuracy of determining the transfer functions, in particular, with respect to the change in the WOB. Further transfer functions derived can be used to control the surface parameters for drilling.

The environmental impact of oil and gas wells from exploration to abandonment is a primary focus during well planning and drilling. However, there is no industry standard to quantify the impact of complex indicators and associated variables, particularly when the well is drilled in real time. Thus, a quantification method using the associated variables used for real-time well monitoring as the well is drilled and produced can be useful. A new model for the sustainability index for well design and engineering during drilling is presented and validated. This proposed method avoids some of the vagueness of well sustainability and can be used and applied in a practical manner. It is based on various metrics and weightage assigned when a well is drilled and compared to the planned metrics. Evaluating the index for well engineering is based on elements of environmental impact, well design and engineering, the impact of functionality and optimization, well and maintenance costs, health and safety, and societal impact. Each element contains subelements. This process involves individual indexing through backpropagation of neural networks combined with the bat algorithm to obtain the echo location to identify the overall index. The proposed method uses a well-engineering-based approach that defines boundaries and thresholds as the well is drilled. The Powell Method, designed for nonconstraint optimization, is also used for fast convergence for this derivative-free problem. The algorithm is fast and provides results based on an iteration method. Because engineering during the well lifecycle involves several nonlinear system and asymmetric inputs, it has been observed that identifying the effects of input uncertainties and other related calculation uncertainties (i.e., variation and errors in log data, survey data, etc.) create an effect. It was also observed that uncertainty analysis provided an optimized index without assigning preferential weightage to some of the components. The method does not reduce uncertainty, but can be used to estimate the influence of various parameters on the sustainability index. To achieve runaway uncertainty, a mutation strategy is introduced to maintain the population diversity of the indices within the defined limits.

Although visual data analytics using image processing is one of the most growing research areas today and is largely applied in many fields, it is not fully utilized in the petroleum industry. This study is inspired by medical image segmentation in detecting tumor cells. This paper uses a supervised Machine Learning technique through video analytics to identify bit dullness that can be used in the drilling industry in place of the subjective screening approach. The evaluation of bit performance can be affected by subjective evaluation of the degree of dullness. The present approach of using video analytics is able to grade bit dullness to avoid user subjectivity. The approach involves the use of datasets in good quantity and quality by separating them into training datasets, testing datasets, and validation datasets. Due to the large datasets, Google Collaboratory was used as it provides access to its Graphic Processing Unit (GPU) online for the processing of the bit datasets. The processing time and resource consumption are minimized using Google GPU. Using the Google GPU resources, the procedure is automated without any installation. After the bit is pulled out and cleaned, a video is taken around and up and down in 360°. Further, it is compared against the green bit. By this approach, multiple video datasets are not required. The algorithm was validated with new sets of bit videos and the results were satisfactory. The identification of the dullness or otherwise of each screened bit is done with the aid of a bounding box with a stamp of a level of confidence (range 0.5–1) and the algorithm assigns for its decision on the identified or screened object. This method is also able to screen multiple bits stored in a single place. In an event where several drill bits are to be screened, manual grading will be a huge task and will require a lot of resources. This model and algorithm will take a few minutes to screen and provide grading for several bits while videos are passed through the algorithm. It has also been found that the grading with the video was much better than the single image as the contextual information extracted are much higher at the level of the entire video, per segment, per shot, and per frame. Also, methodology is made robust so that the video model test starts successfully without error. The time penalty for the processing is fast and it took less time for a single video screening. The work developed here is probably the first to handle the dull bit grading using video analytics. With more of these datasets available, the future automation of the IADC bit characterization will soon evolve into an automated process.

Environmental impacts due to exploration to the abandonment of oil and gas wells are a major focus when the wells are planned and drilled. However, there is no industry standard to quantify the impact due to complex indicators and associated variables. So, it is necessary to have an assessment framework and a methodology with the help of the digital transformation that provides an integrative index. This index allows a baseline to estimate the benchmarking for the well engineering not only during planning but also in real-time as the well is drilled and produced. This paper presents and validates a new model for the sustainability index for well design and engineering in the life cycle of the well. This proposed method avoids some of the vagueness of well’s sustainability and can be used and applied practically. It is based on various metrics and weightage assigned when a well is planned, designed, and engineered. Evaluating the index for well engineering is based on the following elements: environmental impact, well design and engineering, functionality and optimization impact, impact of well and maintenance costs, health and safety impact, societal impact. Each element contains sub-elements. This process involves individual indexing through backpropagation of neural networks combined with bat algorithm to obtain the final overall index.

The syllabus getting outdated, classroom attendance getting less importance, fast advancements of technology and changing workforce, and demography require us to rethink and re-examine the core curricula being taught at petroleum schools. The changing landscape like clean energy and carbon neutral delivery are adding pressure to re-examine the subjects taught in the classroom so that the long-term sustainability is established. So, acquiring interdisciplinary skills is crucial with the reformed curricula. The questions to be addressed include: "What is the fundamental problem in the present petroleum education?," "Is there any problem with the present theoretical framework?," "Is the petroleum education aligned with the latest developments such as edge devices, sensors, machine learning and artificial intelligence?," "Is there an academia-industry-regulatory agencies tighter participation?," and "What are the structural changes needed like rebranding as energy engineering?." The paper addresses these questions by proposing a new approach to petroleum engineering education by way of a changed energy engineering program, which involves fundamentals of engineering, sciences, and technologies that culminates in the development of experiential learning on cyber-physical systems.

Tortuosity is one of the critical factors to be considered for complex directional well trajectories, complicated build rates, precise steering in thin reservoirs, and extended reach wells. This paper discusses the pitfalls of estimating tortuosity to quantify borehole quality and answers questions, such as whether the claimed benefits (i.e., enhanced drilling performance, improved hole cleaning, ease of running casing, and superior cement operations) can be fully attributed to reduced borehole tortuosity. Running casing may mask the tortuosity present in the as drilled open hole wellbore section. This vanishing tortuosity alters the apparent "wellbore quality" and the new tortuosity representative of the cased hole path may present new appearing tortuosity. Both vanishing and appearing tortuosity are generally neglected in engineering calculations. Conventional methods to calculate tortuosity are based on the predetermined shape of the trajectory using the minimum curvature method. Wellbore undulation (geometrical tortuosity) is determined using geometrical measurements such as inclination, azimuth, and calculated displacement; however, much of this wellbore undulation vanishes after the casing is run, and thus the cased off wellpath appears smoother. This apparent change in wellbore tortuosity results from the flexural stiffness and rigidity of the casing pipes, and the compression and tension loads along the length of the casing string. Acquiring a subsequent survey along the cased well path yields new inclinations, azimuths, and displacements. This new survey records wellpath undulations resulting from the casings path through the original open hole wellbore geometry and what we call tubular undulation (mechanical tortuosity) which is specific to the path and position of the casing within the wellbore. The smoothing of the wellpath resulting from the casing masking original wellbore tortuosity results in the original geometrical tortuosity vanishing while the new undulations resulting from the mechanical tortuosity of the casing causes additional tortuosity to appear. The comparison between the geometrical and mechanical tortuosity provides a method of quantifying the vanishing and appearing tortuosity.

In a fast drilling environment, suchas shale drilling, refining advanced technologies for preventing downhole toolfailures is paramount. Challenges are still very much associated with complex bottom-hole assemblies and the vibration of the drill string when used with a downhole mud motor. The mud positive displacement motor with various lobe configurations and designs becomes an additional excitation source of vibration. Further, it affects the transient behavior of the performance mud motor. Unbalanced force exists because the center ofmass of the motor rotor does not coincide with the axis of rotation.Further, the vector of full acceleration of the center of the rotor can be decomposed into two perpendicular projections—tangent and normal—which aretaken into account and integrated intothe full drill string forced frequency modelas force and displacement at the motorlocation. The paper includes two models, first one to predict the critical speeds and the second one to see the transient behavior of the downhole parameters when the mud motor is used.The model also considers the effect of the stringspeed. The unbalanced force is more pronounced at the lower pair or lobe configuration as compared to the higher pairlobe configuration because of the larger eccentricity. The unbalance is modeled in terms of an equivalent mass of therotor with the eccentricity of the rotor. Also, the analysis provides an estimation of relative bending stresses, shear forces, lateral displacements and transient bit rpm, bit torque, and weightone bit for the assembly used. Based onthe study, severe vibrations causing potentially damaging operating conditionswhen transient downhole forcing parametersare used for the vibration model.It has been found that when a mud motor isused using static forcing parameters may not provide the conservative estimation of the critical speeds as opposed totransient parameters. This is because coupled oscillations fundamentally can create new dynamic phenomena, which cannot be predicted from the characteristics of isolated elements of the drilling system.

During perforating operations, identifying the orientation of fiber cable accurately is critical for maintaining the integrity of permanently installed fiber.Beyond completions,it alsoprovides insights into how the casings get twisted and how the mechanical stability of the casing is altered as the string is run in the hole. The drilling and completion system is as unique as the aspect ratio and length/diameter is very high. This puzzles the researchers in modeling forces, stresses, stretch, and twists. To aid the accurate prediction in the position of the casing, radial orientation of downhole fiber optic cables canbe used. The clear images obtained by mapping the equipmentoutside thecasing provides not only how the casings get twisted after running in but also provide improved risk mitigation for perforating operations.The orientation angle of the casing versus depthis then analyzed to get the finaltwist and pitch of the twist of the casing. Several wells datawere analyzed to get a comprehensive view of the casingtwist as the casings were run and versus the model prediction. The raw data obtained using the pulsed-eddy current time-domain decay at each station are used for the analysis. Each installed cable detection clamp (CDC) is placed above a casing centralizer located 2' above each joint of casing that had a clamp installed.This simplifies the process of locating the depth of each CDC. A casing collar locator easily identifies the casingjoints.Further, the data are used to find the casing rotation. Several wells showed normal casing rotation of 2–3 wraps along the lateral and onewell showed more than 12 wraps. Several reasons were considered and analyzed including the wellbore spiraling, borehole torsion,and additional mechanical forces applied duringrunning the casing. The coupling of the geometrical and mechanical twist and mechanical stability of the string are discussed in the paper withmathematical underpinnings. In thecase of abnormal prediction, additional mechanical forcesandgeometrical considerations were overlapped and comparedagainst the torque and drag model prediction.It has also beenfound that in some wells where the wellbore torsion washigh,it resulted in a complete twist of 360° atthe heel and in some cases negative trend.

Annular fluid expansion analysis (AFEA) is a vital step when designing an oil/gas well or geothermal well. Traditionally, an AFEA is performed between a drilling operation (initial condition) and a production operation (final condition). However, well design engineers desire to perform an AFEA between any two operations. The objective of this work is to address that challenge. Sequential AFEA refers to the procedure in which output data (e.g., fluid volume change) of one AFEA are used as input data for another AFEA; the underlying operations are “sequential”. As a result, an AFEA can be configured between any two operations, provided that the “initial condition” operation is before the “final condition” operation. This two-pass AFEA model has been implemented in computer programs and has been integrated with a commercial software tool, including a wellbore thermal flow simulator and tubular stress analyzer. Offshore well examples were used to validate the model. Case study results indicate that a sequential AFEA is in good agreement with a traditional one-pass AFEA in terms of both forward and backward operation sequences.

I titled this Technology Focus introduction “Wellbore Tubulars—When It Becomes Useless.” This may sound negative, but many advancements have been achieved in extending the life of the tubulars and maintaining tubular integrity. The question that remains in front of us, however, is how to monitor tubular integrity in real time. Managing well barriers and maintaining tubular integrity within limits is challenging for complex and aging wells because of many intervening variables. Material and strength degradation of tubulars subjected to constant or varying and concentrated or distributed loads during various operations result in different kinds of tubular failures. Predictive tools are needed for direct and indirect estimation of tubular integrity. Advances have been made to quantify metal loss effectively, thereby estimating strength degradation in a better way. Digital technology and high computing and processing speeds and novel technologies are providing additional impetus to extend the life of tubulars for long-term sustained production, minimizing negative effects on the environment. Recently, many new technologies borrowed from the horizontal-drilling industry have been applied effectively for downhole conditions to detect abnormalities in the tubulars. Now technology has advanced to monitor the metal loss of multiple casings at the same time. Some of the novel technologies involve microwave technology, pulsed-eddy-current technology, magnetic flux leakage, advanced sensors, and sensing technology based on fiber optics. I have selected a few papers that are structured toward this end, and readers can find additional reading on the OnePetro online library.

Oil and gas operators have a multitude of tasks to perform for smooth business operations. In addition to various engineering, technical, and operational decisions, there are back-office operations that should be efficient to keep business running smoothly, including production royalty management (PRM). The current system for PRM is extremely complex and costly, and involvesmultiple parties/contracts and often causes disputes. A blockchain-based solution allows the organizations to better navigate complex laws, courtrulings, and legal jargon that determine how billions of dollars are distributed every year. There are two aspects to PRM —royalties owed and royalties received—and the proposed solution caters to both operators and independent mineral owners. The operator should compile production and revenue data monthly and distribute it to all owners as per their individual lease. The expenses and marketing costs should be entered into the accounting system before revenue is distributed. Owners may be skeptical about data shared; therefore, the entire process can take up to two or more months. Furthermore, privacy is of utmost importance because theoperator can neither share data between owners nor disclose proprietary information. This paper proposes the entire royalty management system be hosted on a permissioned blockchain. Given their intrinsic nature of trustworthiness and transparent execution of transactions involving multiple parties, blockchain can streamline the royalty distribution process. The design and implementation of a smart contract for consent-driven and double-blind data sharingon a blockchain platform is presented. The system is designed for both inter- and intra-organizational transaction—a local peer interacts with an associated anchor peer within the globalnetwork. In addition to transparency and efficiency, this method provides important features ofprivacy and auditability while limiting ledger size. Further, peer identities are hiddenfrom each other while they collectively agree on transaction outcomes, and data will never be shared among the peers of the blockchain without explicit consent from the owners to ensure that the conflicting objectives of privacy and transparency are met. Blockchain is peer-to-peer decentralized digital ledger technology with promising potential. Even after all the recent innovations, the owner-relations department still operates using old methods. An innovative blockchain-based solution to reduce time and money spent on disbursing revenue for the operator and to increase the time value of information and trust in the system for the owner is presented.

The rate of penetration (ROP) was optimized using a particle swarm optimization algorithm for real-time field data to reduce drilling time and increase efficiency. ROP is directly related to drilling costs and is a major factor in determining mechanical specific energy, which is often used to quantify drilling efficiency. Optimization of ROP can therefore help cut down costs associated with drilling. ROP values were chosen from real-time field data, accounting for weight on bit, bit rotation, flow rate variation along with bit wear. A random forest regressor was used to find correlations between the dependent parameters. The parameters were then optimized for the given constraints to find the optimal solution space. The boundary constraints for the ROP function were determined from the real-time data. The function parameters were optimized using a particle swarm optimization algorithm. This is a meta-heuristic model used to optimize an objective function for its maximum or minimum within given constraints. The optimization method makes use of a population of solution particles which act as the particle swarm. These particles move collectively in the given solution space controlled by a mathematical model based on their position and velocity. This model makes use of the best-known solution for each particle and the global best position of the system to guide the swarm towards the optimal solution. The function was optimized for each well, providing optimal ROP values during real-time drilling. A fast drilling optimizer is crucial to automate and streamline the drilling process. This simultaneous optimization of ROP based on real-time data can be implemented during the process thereby increasing the efficiency of drilling as well as reducing the required drilling time.

Several calculation methods have been proposed by the researchers in the past with varying degree of precision. These result in different wellbore position estimation. The survey calculations and positions are used in engineering calculations such as torque and drag, drill ahead prediction, displacement of the string, SAG correction etc. Since the survey calculations are the underpinning starting point for the engineering formulations, considerable errors are introduced in the engineering calculations. Most widely used method in the industry is minimum curvature which assumes a circular arc in an inclined plane and this method is better than some of the previously introduced methods like balanced tangential method, average angle method etc. It has also been seen that, the radius of curvature method is more suitable for a rotary mode of drilling, whereas the minimum curvature method aligns when slide drilling. However, using the minimum curvature method, introduces discontinuity at the survey course intervals. This type of constant curvature model due to discontinuity may result in the omission of some contact forces which will result in the under prediction of the hookload or the stresses in the drillstring components or casing or drillpipe wear calculations. This further results in the discontinuity in the engineering calculations also and will be more pronounced when the wellbore twists and turns. To alleviate this problem, wellbore smoothening is done by using advanced survey methods such as spline, natural arc method, curvature bridging etc. A reliable inclination and directional continuous surveys also enable smooth and continuous wellpath making the problem with the minimum curvature method to vanish. This paper presents the research findings not only on the relationship between the minimum curvature method and engineering calculations but also the inherent problem in the commonly used survey calculation methods. It has been shown that both wellbore curvature as well as the borehole torsion are necessary to relate to the mechanical parameters that are used in the engineering calculations such as bending moment, side force and string position.

Recently, well design engineers attempted to replace traditional API 5CT pipes with cheaper UOE steel pipes in the downhole wellbore construction. The UOE method for producing longitudinally welded large-diameter pipes creates typically weaker pipes in terms of collapse resistance. The standard API 5C3 collapse design procedure no longer meets safety requirements. A practical solution to this issue is discussed. UOE pipe casing can be designed based on the new collapse strength envelope, which is established by modifying the API RP1111 collapse strength model. The original API RP1111 collapse strength model was intended for pipeline design, not downhole tubular design. It is comprised of the elastic collapse term and yield collapse term, with the latter being proportional to material yield strength. However, the deration effects of temperature, tension, and internal pressure are not included. For downhole wellbore tubular design, yield strength is replaced with equivalent yield strength (from API 5C3 new addendum), which is a function of temperature, axial stress, and internal pressure. A new casing design workflow has been implemented in the computer program, and case studies were performed to verify the collapse design results. A new collapse pressure envelope was generated using the computer program integrated with a commercial tubular design tool and was compared to the traditional API 5C3 collapse pressure envelope. As expected, the new collapse strength values, calculated using the modified API RP1111 collapse model, are typically much lower than the estimated values using the API 5C3 collapse formula. Results of the collapse safety factor and maximum allowable wear are also compared between the modified API RP1111 collapse model and the traditional API 5C3 collapse model. Typically, UOE pipe using the modified API RP1111 collapse model generates lower safety factor and maximum allowable wear values, as expected. The API RP1111 collapse strength model has been modified to include the deration effects of downhole conditions. Implementing this model in the commercial tubular design tool enables the cost-effective design of wellbore casing strings using cheaper UOE-manufactured steel pipes.

Modern directional wellbore trajectories are more complex now with some wells having an undulating trajectory. When installing artificial lift devices such as an electric submersible pump (ESP), the pump must be submerged in the liquid phase of the hydrocarbon in order to avoid gas-lock issues, including in shut-in conditions. Incorrect placement of such device would require a very costly remedy. In an undulating trajectory well, it is possible that the fluid in the well is in the gas phase at some of the downhole upper elbow locations during shut-in. This paper presents a method to calculate the water/oil/gas settling interface during shut-in conditions for an undulating trajectory well. In this method, the well is divided into multiple uphill and downhill sections. For each section, it is assumed that the GOR (Gas Oil Ratio) and WOR (Water Oil Ratio) are the same as the production GOR and WOR. The pressure at the elbow turning point of two uphill and downhill sections is assumed to be the same to ensure the pressure continuity. Then, by the given surface or bottom (reservoir) pressure, the gas/oil and oil/water interfaces are calculated with iterations with a numerical method to match the GOR and WOR considering the wellbore pressure and temperature effect for each section. Once all the sections are calculated, the final results are combined to achieve the global gas/oil and oil/water interfaces and the pressure and temperature profiles. This method has been implemented in an advanced casing and tubing design application to calculate the well pressure and temperature profiles, and then these profile results are applied later for further stress analysis. A typical undulating well is presented for case study to explain the calculation steps and results. As expected, the gas/oil and oil/water interfaces are obtained where the water is settling at the lower elbow of the well, the gas phase is accumulated at the upper elbow of the well, and the oil phase stays in the middle between the gas and water. The pressure profile is achieved showing different pressure gradients of the gas/oil/water settling intervals and pressure is continuous along the whole well. The temperature profile is also obtained, showing different temperature behavior for gas, oil, and water intervals with different shut-in duration, which is useful for stress analysis in the string at different depths. This paper fills a gap that has not yet been discussed in the industry literature. It is an important for field application, especially for choosing the artificial lift device installation depth. Further, the calculated pressure and temperature profiles are useful for casing and tubing design stress analysis purposes.

The model of "learn at school" and "do at work" is outdated and must keep up with the changing environment of the Industrial Revolution 4.0 (IR 4.0). The petroleum engineering curricula, therefore, must also change accordingly and this change is needed at a time the new generation of students entering the workforce is different from the generation leaving the workforce. It is time to examine the core curricula being taught at petroleum schools so that long-term sustainability can be established. The petroleum education has been modified very little in the past 30 years and needs to change in the post-Google era to cater to the tech-savvy minds of the modern student. Although developments in technology have positively responded to the needs of the industry in the past, its future advancement will include areas such as telemetry, sensors and data handling, and analytics. Thus, a new approach to engineering education, involving the fundamentals of engineering sciences that culminates in the development of experiential learning on cyber-physical systems, is important. The paper presents and lays out the urgent need for a new type of curriculum for petroleum engineering education that encompasses engineering, science, and technology.

The 1980s oil industry crash, which was caused by an increase in production and a slowdown of the world economy, resulted in a huge surplus of crude oil. Improved horizontal drilling, completion technologies and the underpinning of engineering advancements were major contributing factors. The crude oil price has continued to fall since mid-2014, and has dropped from $106 per barrel. This paper examines the supply and demand of the future workforce and the warning signs that were ignored before the crash. The framework of questions to be addressed includes the following: "What is the fundamental problem in the industry and petroleum universities?", "Is it throttling the advancement of the petroleum industry?", and "What are the changes needed to meet the shortage of future engineers?" Although several challenges are inherently present, the article will particularly ponder the following primary challenges: talent pipeline and shortage, educational curriculum drought, petroleum education intake reform, academia-industry tighter participation, knowledge base erosion, and technical talent flight. A surge in university enrollment has tilted the balance of the student and aging faculty ratio. The key takeaways from this paper include the challenge areas in education and how the industry is responding to its needs, outlining the emerging future direction to meet the challenges of the cyclical industry, as well as climate change pressures. This paper presents the future state of the petroleum program, which will be in a state of stunned growth similar to other industries, like mining and nuclear energy. It also shows a link to environmental pressures.

It is generally acceptable to use soft string model for simple wells and stiff string for high dogleg wells. But these models fail to reveal the complex mechanical behaviors of tubular string in tripping in, tripping out, and sliding drilling operations with time as some segments of tubular string may move upward and downward alternatively. As a result, the model under predicts or over predicts the behaviors of tubular string, and requires multiple calibrations of friction factor. To avoid this, a new model has been developed to estimate dynamic torque and drag. In the new model, the effects of tubular vibration, drill fluid, viscous force, and velocity-dependent friction force are all considered, which overcomes the shortcomings of all previous models. The new model can be used for calculating dynamic mechanical behaviors of tubular string under tripping in, tripping out, and sliding drilling operations. The results provide new insights that have not been previously presented and will provide valuable insights into well planning and designing.

Drilling activities are risky and costly, especially when performed offshore. Careful monitoring and real time data analysis are required for safe and efficient operations with minimized down-time. Drilling operations, being fast-paced and not visible, often lead to transient and unforeseen issues. The synchronous assessment and prediction of drilling quality has historically been a challenge. It relies on a prompt collection, analysis and prediction of the multiple sensors data, as well as an immediate comparison to the original drilling plan. Another challenge is achieving real-time well engineering, and automatically and instantaneously providing valuable insights to the engineering and operations teams. A system was successfully developed to tackle these challenges. It is a cloud-based application, made with an event-driven streaming architecture to automatically retrieve real-time drilling data and compare it with planned data. The real-time data is automatically made available to determine the current well operation or rig state, and trigger the subsequent engineering analysis. Next, a forecast model is trained with the engineering calculation outputs and it returns predictions on these outputs while considering their inherent uncertainty. As a result, these predictions enable alerts to be sent when the system detects approaching anomalous conditions. The proposed system is a DecisionSpace® 365 cloud-native application on an open architecture. It is flexible, accessible from anywhere, can be automatically updated for continuous improvement, and can be deployed easily and quickly. It can also be extended to further applications.

Wear factor is an important parameter for estimating casing wear, yet the industry lacks a sufficient data-driven wear-factor prediction model based on previous data. Inversion technique is a data-driven method for evaluating model parameters for a setting wherein the input and output values for the physical model/equation are known. For this case, the physical equation to calculate wear volume has wear factor, side force, RPM, tool-joint diameter, and time for a particular operation (i.e., rotating on bottom, rotating off bottom, sliding, back reaming, etc.) as inputs. Except for wear factor, these values are either available or can be calculated using another physical model (wear-volume output is available from the drilling log). Wear factor is considered the model parameter and is estimated using the inversion technique method. The preceding analysis was performed using soft-string and stiff-string models for side-force calculations and by considering linear and nonlinear wear-factor models. An iterative approach was necessary for the nonlinear wear-factor model because of its complexity. Log data provide the remaining thickness of the casing, which was converted into wear volume using standard geometric calculations. A paper [1] was presented in OMC 2019 discussing a method for bridging the gap. A study was conducted in this paper for a real well based on the new method, and successful results were discussed. The current paper extends that study to another real well casing wear prediction with this novel approach. Some methods discussed are already included in the mentioned paper.

The high-frequency downhole vibration data include a greater amount of hidden information than the low-frequency surface data. This paper proposes the monitoring of high-frequency acceleration data for early kick detection. The trend of accelerator sensor values is monitored, rather than processed. When the gas, fluid, or oil kick occurs, the fluid influx reduces the viscosity of the fluid in annulus, which causes the degradation of the damping factor. The sensor installed on the drillpipe detects the velocity/acceleration change that results in the damping factor change. This approach includes an analytical model to calculate the effect of the damping ratio on the acceleration calculations. The fluid influx and migration in the wellbore strongly affect the damping factor. The paper presents a method of deconvoluting the sensor values that uses a combination of minimum entropy deconvolution and Teager-Kaiser energy operator to remove the noise, unwanted sensor values, and likelihood of false prediction. It is then proposed to calculate instantaneous jerk and jerk intensity at each depth. The trend of the final intrinsic mode functions (IMF) at each depth is continuously monitored to predict the formation influx, if any. A novel concept of monitoring the incremental IMF and IMF energy at each depth is introduced. This technique is shown to reveal a wealth of information and simplifies the process of monitoring and analyzing the vast amount of available data. The methodology developed is applied to extract the essential information from high-frequency vibration data to make real-time data monitoring straightforward, reliable, and fast.

Within both productive and nonproductive time (NPT), a drilling operation can include invisible lost time (ILT), which is the time lost because a portion of the drilling operations is not performed at maximum efficiency. Within this ILT, another dimension of lost time can exist because of the inherent nature of the process, use of unoptimized equipment, or because the process might not have been performed at the optimum efficiency. Both NPT and ILT concepts have been discussed in the literature. Some concepts are based on the maximum theoretical performance (MTP), and others are based on threshold time calculation. Regardless of the estimation method used, NPT and ILT are generally quantified in the literature as products of downhole conditions. Another dimension of NPT: microinvisible lost time (MILT) is introduced in this work. MILT is identified as being affected by two major factors: surface equipment design and efficiency and process optimization. MILT also includes ergonomic human inefficiencies attributable to these factors. A case study is presented to identify the MILT using an online statistical analysis method. It can be integrated with future management and well design plans to improve the efficiency of drilling operations. The objective is to help identify the commonly ignored hidden factors that contribute to NPT; consequently, it can help promote faster, more efficient, and more cost-effective drilling.

Summary The internet of things has brought better connectivity among devices in various industries including the oil and gas industry. With this improved connectivity comes improved applications, especially in the area of real-time data processing and analytics. A major requirement for real-time application is minimized latency. Cloud computing, despite its many benefits, is often limited in that area. A lag often starts and worsens when the realtime data has to go back and forth between field locations and data centers. On the other hand, edge computing enables faster processing speeds. Edge computing applied to real-time well engineering is presented and discussed. It has the potential to be an adequate approach for applications dependent on real-time processing such as geosteering, which requires instantaneous processing to drastically enhance the economic profitability of a well. With edge computing, some sensor data can be cleaned and processed instantaneously to automatically identify the rig state, which is a key step in the real-time well engineering workflow during drilling.

How we think about the future of the pipe industry must evolve. How must tubular design and manufacturing change as we transition to clean energy? Geothermal energy is an area that needs attention and, further, needs very specific attention on tubulars. Tubulars are an important component in the construction of geothermal wells, and we must align our requirements for geothermal energy. Some of the main challenges encountered in geothermal wells are corrosion and scaling. Moreover, temperature becomes a major consideration for tubulars, even more so with the temperature excursion during geothermal production. Perhaps the critical aspect in the design of the geothermal wells involves casing selection and design. Beyond manufacturing casing pipes to withstand these problems, considering the manufacturing of other components, such as connections, float collars, and float shoes, also is essential. Thermal expansion and thermal excursion of casings are well-integrity concerns; thus, casing design is important for long-term sustainability of geothermal wells. Apart from thermal simulations, guidelines and software are needed to undergird the designs to withstand not only temperature excursions but also thermomechanical and thermochemical loadings. Engineered nonmetallic casings also provide an alternative solution because they provide the desired strength and corrosion resistance in addition to meeting the goals of sustainability. Undoubtedly, the future of the tubular industry is going to be revitalized. The question now is how we can retrofit existing abandoned wells for this purpose. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199570 - Special Considerations for Well-Tubular Design at Elevated Temperatures by Gang Tao, C-FER Technologies, et al.

Summary Traditionally, the collapse safety factor (SF) is the ratio of tubular-collapse resistance to collapse load. The collapse resistance is usually calculated by use of formulas suggested by API TR 5C3 (2008) or ISO/TR 10400 (2007). If the collapse load is very small, whereas the tension stress and/or internal pressure are high, the collapse SF could be very large, which is unrealistic because the load point is close to the collapse envelope. This paper presents a vector approach for the collapse-SF calculation to overcome this problem. Moreover, the application of this vector approach can be properly extended to the calculation of International Organization for Standardization (ISO) connector SF for arbitrary load combinations. The new SF is named the vector SF. It is the multiplier that would scale the load-point vector to reach the strength envelope in stress space. It must meet the following requirement: When all the tubular loads (axial force, internal pressure, and external pressure) are scaled by this SF, the load point must fall exactly on the failure envelope. Therefore, for American Petroleum Institute (API) collapse, the new SF must simultaneously satisfy the API TR 5C3 (2008) collapse formulas and the through-load-point radial-line equation. Ridders' method (Ridders 1979) is used to solve the SF for a specific load case. The algorithm has been implemented in a casing-design computer program (Landmark 2017) and integrated with commercial software. This vector approach has been applied to both pipe body and connectors. Example cases are used to study the effect of the vector approach on the collapse-SF values. The collapse-SF value is observed to become much smaller when the effective axial stress (actual axial stress plus internal pressure) is very high, which is expected because of the much-smaller vector collapse resistance (at the cross point of the radial line with the collapse curve). When the effective axial stress is not positive, the collapse SF slightly changes. A North Sea high-pressure/high-temperature well is also presented. The results indicate that the vector collapse SF is more realistic. In particular, it is most conservative in load cases with high effective axial stresses. In summary, it is shown that the vector approach to calculate the SF is equivalent to the current method of comparing the von Mises equivalent stress with the material yield strength (YS). The vector SF has critical safety implications for load cases in which high effective axial stresses exist. Its software implementation can certainly assist well engineers with more-conservative and more-straightforward tubular design.

During the lifetime of an oil/gas well, wellbore tubular structure might be subject to combined damage caused by both corrosion and mechanical wear. Therefore, it is necessary to conduct detailed stress analyses including these factors at the stage of tubular design. An integrated well construction workflow was established for life-time well design. The temperatures and casing/tubing loads were obtained through numerical simulations of operations such drilling, stimulation, and production. All these simulations were accomplished using commercial software tools, including a thermal flow simulator and stress analyzer. On one hand, a commercial casing-wear simulator was used to predict the cumulative wear amount. On the other hand, a corrosion simulator was employed to predict pipe metal losses during each operation. The total amount of corrosion loss and mechanical loss were then compared against the maximum allowable wear for a safety check of the design. The corrosion simulator was implemented in a computer program and integrated with the aforementioned commercial software of thermal flow and stress analysis. In a plot of maximum allowable wear versus depth, the curves of predicted wear, predicted corrosion, and predicted total metal loss are superimposed with the maximum allowable wear. This plot gives a straightforward and clear picture of the overall lifetime safety of the design. A field case was studied with those integrated simulations. The production casing internal wear and internal/external corrosion were simulated. The predicted wear and corrosion data were in good agreement with the measured results. Further predictions provide rationales for future maintenance/workover operations. Corrosion simulation and casing wear simulation were coupled with wellbore thermal flow analysis and stress analyses, helping proactively prevent tubular failure during the lifetime of the well. It is therefore valuable to include the integrated workflow during the wellbore tubular design where both corrosion and wear are involved.

The soft string and stiff string models are different string methods that have been used by the oil industry to calculate torque and drag for years. Opinions have often varied as to which model is better. This paper discusses the intrinsic difference between these two models and proposes a criterion for determining which method would deliver the most accurate results. Although both the soft string and stiff string models are used for torque and drag calculation, the soft string model is calculated like a soft rope, without considering the influence of the hole size and radial clearance. On the other hand, the stiff string model considers the effect of stiffness, thereby accounting for the extra side contact force. In theory, the stiff string model is more accurate because more variables are considered. However, the field data is more inclined to match the soft string model as opposed to the stiff string model. In fact, many factors, including the stiffness of the string, the shape of the wellbore, and clearance, can influence the status of the string and the value of the contact side force on the string. The present stiff string model does not consider these factors, making it difficult to predict accurate results. This paper analyzes the physics statuses of the string under different scenarios in order to determine which model to apply. Results demonstrate that borehole tortuosity and the shape of the wellbore can significantly change the status of the string. All factors are relative. A string with a large-size section can be very soft in a straight wellbore, which is fit for a soft string model. Likewise, a string with small-size section can be very stiff in a wellbore with severe tortuosity, which is a better fit for a stiff string model. To accurately estimate the drag force, the stiffness, as well as the wellbore shape and its clearance, should be considered. Extensive simulations have been performed and are reviewed in this paper. Results confirm that the soft string model is a better choice when the string is slimmer, the wellbore is in a lower curvature shape, and the clearance is larger. On the contrary, the stiff string model is more useful when the string is stronger, the wellbore is in a high curvature shape, and the clearance is lower. When to use the models depends on the bending shape of the string in the wellbore. Since neither model is equipped to handle all scenarios, combining the two methods provides better results.