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The demand for oil and gas is continuing to rise with a growing population and worldwide industrialization. Surfactants are considered excellent additives for drilling formulations because of their unique properties and chemical structure. The surface-modified nanoparticles in the drilling fluids (DFs) help to improve the rheological and filtration properties of water-sensitive shale formations. The water-sensitive shale formations in the wellbore often result in swelling after interacting with water-based DFs. The swelling of shale formation impacts the rheological and filtration properties of DFs. The aim of this study is to formulate DFs with cationic surfactants and surface-modified nanoparticles to minimize shale swelling and improve the rheological and filtration properties. Various drilling fluid formulations were prepared with bentonite as a basic constituent of DFs while Gemini surfactant and graphene nanoparticles were added with concentrations of 0.5%. The rheological and filtration properties were determined at room temperature. The shale inhibition tests were performed to analyze the swelling inhibition properties of DFs. The surface-modified nanoparticles along with the cationic surfactant make a stable dispersion of DFs. The presence of nanoparticles in the DFs enhances the rheological and filtration properties. The filtrate loss has been significantly reduced by incorporating graphene nanoparticles and Gemini surfactant-modified graphene nanoparticles. The rheological properties such as plastic viscosity, yield stress, and gel strengths have been improved by the combined addition of surfactant-modified nanoparticles. The reduction of filtrate loss was due to the clogging effect of small passages in the filter cake while long alkyl chains of surfactant molecules spread over the filter cake making a hydrophobic film that minimizes the contact of water with the filter cake. Moreover, the swelling inhibition test such as the linear swelling test showed that the presence of nanoparticles and cationic surfactants significantly enhanced the shale swelling inhibition and reduced the percentage of swelling compared to the DI water. The cationic surfactant interacts with the negatively charged clay particles through electrostatic forces and surfactant along alkyl chains wraps around the clay particles which leads to the minimum swelling of shale formations. This study reveals that the formulations based on surface-modified nanoparticles and surfactants in water-based DFs can inhibit shale swelling and improves the borehole stability for water-sensitive shale formations.

Low permeability rock usually holds a large amount of residual oil after flooding. The two most important mechanisms for residual oil recovery are interfacial tension (IFT) reduction and wettability alteration (WA). There is confusion around the coupled effect between the two mechanisms. Permeability is found to be a critical factor on the coupled effect. In this study, the spontaneous imbibition oil recovery results from core plugs of different permeability by using two surfactants were compared. The comparison helps understand the impact of permeability on the coupled effect of IFT reduction and WA. Filtered crude oil (density 0.87 g/mL, viscosity 12.492 cP), Indiana limestone cores of different permeabilities, two locally synthesized cationic gemini surfactants, GS3 and GS6, were used in this study. The spinning drop method and static contact angle method were used to measure the oil/water IFT and the wettability. Spontaneous imbibition experiments using Amott cells were conducted at the ambient condition to relate IFT reduction and WA performance to the oil recovery contribution. Results showed that although the selected surfactants had comparable IFT reduction performance, GS3 is much stronger than GS6 in altering oil-wet carbonate rock to water-wet conditions. In core plugs with the same dimensions and comparable low permeabilities, the oil recovery values accorded with the WA performance. GS3 obtained faster and higher oil recovery (24%) than and GS6 (14%), indicating that enhancing WA alone contributes to oil recovery. The main difference between the selected surfactants was the spacer structure. It appeared that introducing unsaturation into the spacer group harmed the WA performance. Comparing different permeability conditions, GS6 obtained much higher oil recovery in a high permeability condition (922 mD) than in a low permeability condition (7.56 mD). Though permeability significantly impacted the whole imbibition process, it was more auspicious when IFT reduction became the main driving force. This study studied the WA mechanism alone by adopting surfactants with comparable oil/water IFT values. It also features the impact of permeability by comparing the recovery curve by the same surfactant under different permeability, showing that IFT reduction contributes more to oil recovery in high permeability rock.

Clay swelling and dispersion in tight sandstones can have an influence on the formation's mechanical properties and productivity. Hydraulic fracturing is a typical stimulation technique used to increase the production of sandstone formations that are too compact. The interaction of clay in sandstone with a water-based fracturing fluid causes the clays to disperse and swell, which weakens the rock and reduces its productivity. Several swelling inhibitors, including inorganic salts, silicates, and polymers, are regularly added to fracturing fluids. Concerns linked with these additions include a decrease in production owing to formation damage and environmental concerns associated with their disposal. In this study, we introduced naturally existing material as a novel green swelling inhibitor. The performance of the novel green inhibitor was examined by its impact on the mechanical properties of the rock. Acoustic strength and scratch tests were conducted to evaluate rock mechanical parameters such as unconfined compressive strength. Further inhibition potential was evaluated by conducting linear swell and capillary suction timer tests. The contact angle was measured on a sandstone surface for wettability change. The results showed the novel green additive provided strong inhibition to clays. The reduction in linear swelling and rise in capillary suction time showed the inhibition potential and water control potential of the biomaterial. Furthermore, mechanical properties were lower than DI-treated rock sample tested under dry conditions. With all these benefits, using green novel additive makes rock more stable and reduces damage to the formation. The green additive is economical and an environment-friendly solution to clay swelling. It is an effective recipe for reducing the formation damage caused by clay swelling.

When drilling oil and gas wells, hole cleaning efficiency is crucial, particularly in the curved or severely deviated sections. Although many hole-cleaning procedures and models have been developed, most of them have substantial limitations or are difficult to apply in real time. This study aimed to develop a model for the hole cleaning index (HCI) that could be integrated into the drilling operations to provide an automated and real-time evaluation of deviated drilling hole cleaning. The new model herein was developed based on the mechanical drilling parameters, enhanced estimated drilling fluid properties, and cuttings characteristics. This HCI model was validated and tested in the field, as it was applied when drilling 12.25”-intermediate directional sections in two wells with a total length of approximately 2000 ft each. The integration of the HCI helped to attain a much better well drilling performance (50% enhancement) and mitigation of potential problems like pipe sticking and the slower rate of penetration. Since the developed index incorporates the changes in wellbore geometry and other spontaneous field data, the new model could be utilized for real-time optimization and intermediate interventions by drilling teams, unlike commercial software tools which are only useful during the planning phase. For this reason, the HCI can be linked to the driller's control panel to provide timely evaluation and corrective measures related to hole cleaning.

A well production rate is an essential parameter in oil and gas field development. Traditional models have limitations for the well production rate estimation, e.g., numerical simulations are computation-expensive, and empirical models are based on oversimplified assumptions. An artificial neural network (ANN) is an artificial intelligence method commonly used in regression problems. This work aims to apply an ANN model to estimate the oil production rate (OPR), water oil ratio (WOR), and gas oil ratio (GOR). Specifically, data analysis was first performed to select the appropriate well operation parameters for OPR, WOR, and GOR. Different ANN hyperparameters (network, training function, and transfer function) were then evaluated to determine the optimal ANN setting. Transfer function groups were further analyzed to determine the best combination of transfer functions in the hidden layers. In addition, this study adopted the relative root mean square error with the statistical parameters from a stochastic point of view to select the optimal transfer functions. The optimal ANN model's average relative root mean square error reached 6.8% for OPR, 18.0% for WOR, and 1.98% for GOR, which indicated the effectiveness of the optimized ANN model for well production estimation. Furthermore, comparison with the empirical model and the inputs effect through a Monte Carlo simulation illustrated the strength and limitation of the ANN model.

Over the last few decades, hydrogen storage has become a vital issue for hydrogen technologies. Several techniques, such as adsorbents, hydrides, nanomaterials, metal–organic frameworks and porous polymers, have been widely explored for hydrogen storage. Although some techniques are promising, there are still challenges, such as operating temperature and pressure, cyclic reversibility and higher hydrogen content. The concept of carbon-based nanomaterials in hydrogen storage, among all the systems that are up-and-coming, appears to be promising, especially the carbon nanotubes (CNTs), activated carbons, and carbon particle systems. This work reports on the development of carbon material from naturally available biomass, such as waste date leafs, through the pyrolysis method and its hydrogen capacity and comparison with commercial CNTs. The synthesised carbon nanomaterial was characterised using field emission scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, Raman spectroscopy, and the Brunauer–Emmett–Teller method. The date leaf carbon nanomaterial was found to have better surface area and pore‐size distribution than CNTs, which is promising for hydrogen storage.

During the drilling operations and because of the harsh downhole drilling environment, the drill string suffered from downhole vibrations that affect the drilling operation and equipment. This problem is greatly affecting the downhole tools (wear and tear), hole problems (wash-out), mechanical energy loss, and ineffective drilling performance. Extra non-productive time to address these complications during the operation, and hence, extra cost. Detecting the drillstring vibrations during drilling through the downhole sensors is costly due to the extra service and downhole sensors. Currently, the new-technology-based solutions are providing huge capabilities to deal intelligently with the data, and machine learning applications provide high computational competencies to learn and correlate the parameters for technical complex problems. Consequently, the objective of this paper is to develop a machine learning model for predicting the drillstring vibration while drilling using machine learning via artificial neural networks (ANN) for horizontal section drilling. The developed ANN model was designed to only implement the surface rig sensors drilling data as inputs to predict the downhole drilling vibrations (axial, lateral, and torsional). The research used 5000 data set from drilling operation of a horizontal section. The model accuracy was evaluated using two metrics and the obtained results after optimizing the ANN model parameters showed a high accuracy with a correlation coefficient R higher than 0.97 and average absolute percentage error below 2.6%. Based on these results, a developed ANN algorithm can predict vibration while drilling using only surface drilling parameters which ends up with saving the deployment of the downhole sensors.

Measuring oil production rates of individual wells is important to evaluate a well's performance. Multiphase flow meters (MPFMs) and test separators have been used to estimate well production rates. Due to economic and technical issues with MPFMs, especially for high gas-oil ratio (GOR) reservoirs, the use of a choke formula for estimating well production rate is still popular. The objective of this study is to implement different artificial intelligence (AI) techniques to predict the oil rate through wellhead chokes. Support-vector machine (SVM) and random forests (RF) were used to generate different models to predict the production rates for high GOR and WC wells. A set of data (548 wells) was obtained from oil fields in the Middle East. GOR varied from 1000 to 9351 scf/stb, and WC ranged from 1 to 60%. Around 300 wells were flowing under critical flow conditions, while the rest were subcritical. Hence, two cases were studied using each AI model. Seventy percent of the data was used to train both RF and SVM models, while 30% of the data was used to test and validate these models. The developed RF and SVM models were then compared against the previous empirical formulas. The RF model in both critical and subcritical flow conditions was able to perfectly match the actual oil rates. SVM was able to predict the general trend for the oil rates but missed some of the sharp changes in the oil rate trend. The average absolute percent error (AAPE) values in the subcritical flow for SVM and RF were 1.7 and 0.7%, respectively, while in the critical flow, the AAPE values were 1.4 and 0.75% for SVM and RF models, respectively. SVM and RF models outperform the published formulas by 34%. The results from this study will help to estimate the real-time oil and gas rates based on the available data from wellhead chokes without the need for field intervention.

Formation damage phenomenon constitutes serious operational and economic problems to the petroleum production. Oil production in certain reservoirs is inadvertently impaired by precipitation and deposition of the high molecular weight components such as paraffin wax. A facile applicability of synergistic effects of thermochemical reaction and ultrasonication to mitigate wax deposition has been presented in this article. Thermochemical heat source has to do with exothermic heat generation from certain chemical reactions. On the other hand, ultrasonication causes cavitation and implosion of bubbles, which is trasimmted as energy in the medium and assit in detaching contaminants from the surface. Series of imbibition experiments were conducted at different ultrasound frequencies (low 28kHz, and high 40kHz), exposure times (20, 40, and 60 mins), and different molarities (M1, M2, and M3) of the thermochemical fluids (TCF), for removing wax deposit from tight Scioto Sandstone core samples. The performance was followed through permeability and porosity tests, as well as Scanning Electron Microscopy with Energy-Dispersive X-ray (SEM-EDX) analyses. Ultimately, the results revealed promising potentials for the proposed technology for efficient paraffin wax removal from a tight rock sample up to 70% within the experimental limits investigated.

Hole cleaning for the majority of vertical and directional drilling wells continues to be a substantial difficulty despite improvements in drilling fluids, equipment, field techniques, and academic and industrial research. Poor hole cleaning might cause issues such as stuck pipe incidents, drilling cuttings accumulation, torque and drag, the erratic equivalent circulating density in the annulus, wellbore instability, tight spots, and hole condition issues. In order to enable the real-time and automated evaluation of hole cleaning efficiency for vertical and directional drilling, the article’s objective is to develop a novel model for the cutting transport ratio (CTRm) that can be incorporated into drilling operations on a real-time basis. The novelCTRmmodel provides a robust indicator for hole cleaning, which can assess complications and enhance drilling efficiency. Moreover, the novelCTRmmodel was successfully tested and validated in the field for four wells. The results of the real-time evaluation showed that the novel model was capable of identifying the hole cleaning efficiency in a normal drilling performance for Well-C and a stuck pipe issue in Well-D. In addition, the novelCTRmimproved the rate of penetration by 52% in Well-A in comparison to Well-B.

The reservoir rock is made up of different minerals and its surface chemistry is influenced by the reservoir environment. Well operations implemented during the life of a field induce changes in th...

Viscosity is the resistance of a material to continuous deformation exerted by shear force. High viscosity, which is sometimes greater than 1 million mPa s, at the initial reservoir conditions, is a major challenge to recovery, production, and transportation of bitumen. Addition of organic solvents or diluents with bitumen leads to significant viscosity reduction and forms the basis for the steam/solvent-assisted recovery methods of extra-heavy oil and bitumen. Therefore, modeling and predicting viscosity of bitumen–solvent mixture has become an important step in the development of solvent-assisted system. The aim of this article is to present a concise survey of the various viscosity models that have been proposed to predict the viscosity of bitumen–solvent mixtures, and make comparative discussion on their applicability. Available reports revealed that the accuracy of a model to predict the viscosity of bitumen–solvent mixtures depends on various factors including the type and concentration of solvents, and the properties of the bitumen. Thus, no model has been found to have absolute capability to predict the viscosity for all mixtures. Therefore, there is room for further improvement on the viscosity modeling of bitumen–solvent system for wider applications.

Scale formation and deposition in the subsurface and surface facilities have been recognized as a major cause of flow assurance issues in the oil and gas industry. Sulfate-based scales such as sulfates of calcium (anhydrite and gypsum) and barium (barite) are some of the commonly encountered scales during hydrocarbon production operations. Oilfield scales are a well-known flow assurance problem, which occurs mainly due to the mixing of incompatible brines. Researchers have largely focused on the rocks' petrophysical property modifications (permeability and porosity damage) caused by scale precipitation and deposition. Little or no attention has been paid to their influence on the surface charge and wettability of calcite minerals. Thus, this study investigates the effect of anhydrite and barite scales' presence on the calcite mineral surface charge and their propensity to alter the wetting state of calcite minerals. This was achieved vis-à-vis zeta-potential (ζ-potential) measurement. Furthermore, two modes of the scale control (slug and continuous injections) using ethylenediaminetetraacetic acid (EDTA) were examined to determine the optimal control strategy as well as the optimal inhibitor dosage. Results showed that the presence of anhydrite and barite scales in a calcite reservoir affects the colloidal stability of the system, thus posing a threat of precipitation, which would result in permeability and porosity damage. Also, the calcite mineral surface charge is affected by the presence of calcium and barium sulfate scales; however, the magnitude of change in the surface charge via ζ-potential measurement is insignificant to cause wettability alteration by the mineral scales. Slug and continuous injections of EDTA were implemented, with the optimal scale control strategy being the continuous injection of EDTA solutions. The optimal dosage of EDTA for anhydrite scale control is 5 and 1 wt % for the formation water and seawater environments, respectively. In the case of barite, in both environments, an EDTA dosage of 1 wt % suffices. Findings from this study not only further the understanding of the scale effects on calcite mineral systems but also provide critical insights into the potential of scale formation and their mechanisms of interactions for better injection planning and the development of a scale control strategy.

The reservoir rock ismade up of differentminerals which contribute to the overall formation wettability. These minerals in their natural state differ in chemistry and structure, and thus behave differently in an environment of varying composition and salinity. These have direct implications for enhanced oil recovery due to water flooding, or wettability alteration due to long-term exposure to brine. With the reservoir rock being a complex system of multiple minerals, the control of wettability alterations becomes difficult to manage. One of the dominant mechanisms responsible for wettability alteration is the mineral surface charge, which is dependent on pH, and fluid composition (salt type and salinity). For the first time, the surface charge development of barite, dolomite, and feldspar minerals in their native reservoir environments (accounting for the formation brine complexity) is presented. Also, the effect of oilfield operations (induced pH change) on minerals’ surface charge development is studied. This was achieved by using the zeta potential measurements. The zeta potential results show that barite and dolomite minerals possess positively charge surfaces in formation water and seawater, with feldspar having a near-zero surface charge. Furthermore, the surface charge development is controlled by the H+/OH− (pH), electrical double-layer effect, as well as ion adsorption on the mineral’s surfaces. These findings provide key insights into the role of fluid environment (pH, composition) and oilfield operations on mineral surface charge development. In addition, the results show that careful tuning of pH with seawater injection could serve as an operational strategy to control the mineral surface charge. This is important as negatively charged surfaces negate wettability alteration due to polar crude oil components. Also, the design of an ion-engineered fluid to control the surface charge of minerals was implemented, and the results show that reduction in the Ca2+ concentration holds the key to the surface charge modifications. Surface charge modifications as evidenced in this study play a critical role in the control of wettability alteration to enhance production.

SummaryA novel approach to exploit heat and pressure generated from the exothermic reactions of the aqueous solution of thermochemical reactants, in removing emulsion blockage induced by oil-based mud (OBM) has been investigated. The proposed technology essentially concerns raising the temperature and pressure of the formation above the kinetic stability of emulsions using thermochemical fluid (TCF). From the batch experiments, to assess the energetics of the thermochemical reaction, it was observed that the temperature of the system could be raised above 170°C at a pressure of 1,600 psi. The chemical can be effectively applied under different operating temperatures Tr = 20, 40, 55, and 100°C without significant effect on the heat and pressure generation. The specific energy per unit volume of the reaction is equivalent to ≈370 MJ/m3 within the operating conditions. OBM was prepared and used as the damaging fluid. A TCF was injected into the damaged core sample for cleaning. Permeability and porosity change of the treated core was tested using nuclear magnetic resonance (NMR) to monitor the efficiency of the TCF injection. Ultimately, injecting 1 pore volume (PV) of the TCF removed approximately 72% of the OBM-based emulsion from the core sample. In addition, permeability of the core sample increased from 120 to 800 md, while the porosity increased from 20 to 21.5% after treatment. Moreover, the pressure profile, observed during the flooding experiment, showed that no precipitation or damage was induced during the TCF flooding. Therefore, it is envisaged that the in-situ heat generation can mitigate the emulsion blockage problem and offer advantages over the existing methods considering environmental friendliness and damage removal efficiency.

Effect of water fraction on the flow characteristics of water-in-oil (W/O) emulsions through porous medium has been investigated by experiments and computational fluid dynamics (CFD). Flooding experiments of W/O emulsions (E1, E2, E3: water fraction, θw (%v/v), = 11.8, 22, to 38%, respectively) and those of original oil (E0, θw = 0) were performed using a packed glass bead porous medium. The experimental information was used in building and validating a single-phase pore-scale flow model using the COMSOL Multiphysics 5.4 Software (base model). The simulated porosity (φ = 0.27) and absolute permeability (Ka = 74 mD) of the base model are in good agreement with those from the experiments (porosity φ = 0.30 and absolute permeability Ka = 68 mD). The simulated pressure drop and effective viscosity also compared fairly well with those obtained from the experiments (R2 = 0.98; and 0.83, respectively). Ultimately, the CFD results show that the water fraction of W/O emulsion increased the viscosity, which subsequently increased the pressure drop. In addition, it was confirmed that the injection velocity and the pore size affect the flow characteristics in the porous medium.

The static Young's Modulus (Estatic) is an important parameter affecting the design of different aspects related to oil and gas producing wells. It is significantly changing based on the type of the formation, and hence, an accurate method of identifying Estatic is required. This study evaluates the performance of support vector regression (SVR) for prediction of the Estatic. The SVR model was learned to evaluate the Estatic from the well logs of the bulk formation density in addition to compressional and shear transit time. It was learned and tested on 592 training datasets of the inputs and their corresponding Estatic, these datasets were obtained from a sandstone formation in Well-A. The learned SVR model was then validated on 38 data points from Well-B, the performance of the optimized SVR on predicting the Estatic for the validation data was also compared with these of the early optimized artificial neural networks (ANN) and functional neural networks (FNN). As a result, all machine learning models showed high precision in predicting the Estatic for the validation data where Estatic was estimated with average absolute percentage errors of 3.80%, 2.54, and 2.03% and correlation coefficients of 0.991, 0.997, and 0.999 using the optimized ANN, FNN, and SVR models, respectively. This result shows the high accuracy of the SVR on predicting the Estatic.

Due to the corrosive nature of sour natural gas, productions through tubing and casing are susceptible to corrosion, resulting in gas leaking. This issue causes massive production loss. Subsequently, well health monitoring and early detection of existing and developing gas leakage are essential for profitable gas production. Dynamic material balance technique estimates gas initial in place (GIIP) using wellhead or bottom hole pressures and gas rates data at flowing conditions during the production. This method is commonly utilized for estimating GIIP but does not apply to predict gas leakage issues. This work aims to add a leak factor term by modifying the dynamic material balance equation and build a mathematical platform to detect and validate the problem by applying the modified equation. The new platform produces expected well behaviour using the revised equation and curve-fitting tool in MATLAB and then compares with the actual behaviour and detect gas leakage. The deviation from the expected and actual behaviour determines the issue. Each component of the model is validated using know values. After that, the total system is tested with known leakage data. Finally, the platform is applied in the active gas well, and the leak detection of the platform is reasonably well. The new workflow can notify the production engineers so they can take corrective measures about the issue.

Fluid losses into formations are a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well control, stuck pipe, and wellbore instability, which, in turn, lead to an increase in well time and cost. This research aims to use and evaluate different machine learning techniques, namely, support vector machine (SVM), random forest (RF), and K nearest neighbor (K-NN) in predicting the loss of circulation rate (LCR) while drilling using solely mechanical surface parameters and interpretation of the active pit volume readings. Actual field data of seven wells, which had suffered partial or severe loss of circulation, were used to build predictive models with an 80:20 training-to-test data ratio, while Well No. 8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. The root-mean-square error (RMSE) and correlation coefficient (

Well-performance investigation highly depends on the accurate estimation of its oil and gas flowrates. Testing separators and multiphase flowmeters (MPFMs) are associated with many technical and operational issues. Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1131 data points includes GOR, upstream and downstream pressures (PU and PD), choke size (D64), and actual data of oil and gas production rates. The data have GOR was up to 9265 SCF/STB, the oil rate varied from 1156 and 7982 BPD. SVM and RF models were built to estimate the production rates. The ML models were trained using seventy percent of the dataset, while the models were tested and validated using 30% of the dataset. The dataset was classified to 622 wells that were flowing at critical flow compared with 509 wells that were flowing at subcritical conditions based on a PD/PU ratio of 0.55. Four machine learning models were developed using SVM and RF for subcritical flow and critical flow conditions. Different performance indicators were applied to assess the developed models. SVM and RF models revealed average absolute percentage error (AAPE) of 1.3 and 0.7%, respectively, in the case of subcritical flow conditions. For critical flow conditions, the AAPE was found to be 1.7% in the SVM model, and 0.8% in the RF model. The developed models showed a coefficient of determination (R2) higher than 0.93. All developed ML models perform better than empirical correlations. These results confirm the capabilities to predict the oil rates from the choke parameters in real-time without the requirement of instrument installation of wellsite intervention.

Polymer flooding is used to improve the viscosity of an injectant, thereby decreasing the mobility ratio and improving oil displacement efficiency in the reservoir. Thanks to their environmentally benign nature, natural polymers are receiving prodigious attention for enhanced oil recovery. Herein, the rheology and oil displacement properties of okra mucilage were investigated for its enhanced oil recovery potential at a high temperature and high pressure (HTHP) in carbonate cores. The cellulosic polysaccharide used in the study is composed of okra mucilage extracted from okra (Abelmoschus esculentus) via a hot water extraction process. The morphological property of okra mucilage was characterized with Fourier transform infrared (FTIR), while the thermal stability was investigated using a thermogravimetric analyzer (TGA). The rheological property of the okra mucilage was investigated for seawater salinity and high-temperature conditions using a TA rheometer. Finally, an oil displacement experiment of the okra mucilage was conducted in a high-temperature, high-pressure core flooding equipment. The TGA analysis of the biopolymer reveals that the polymeric solution was stable over a wide range of temperatures. The FTIR results depict that the mucilage is composed of galactose and rhamnose constituents, which are essentially found in polysaccharides. The polymer exhibited pseudoplastic behavior at varying shear rates. The viscosity of okra mucilage was slightly reduced when aged in seawater salinity and at a high temperature. Nonetheless, the cellulosic polysaccharide exemplified sufficiently good viscosity under high-temperature and high-salinity (HTHS) conditions. Finally, the oil recovery results from the carbonate core plug reveal that the okra mucilage recorded a 12.7% incremental oil recovery over waterflooding. The mechanism of its better displacement efficiency is elucidated

Hole cleaning efficiency is one of the major factors that affects well drilling performance. Rate of penetration (ROP) is highly dependent on hole cleaning efficiency. Hole cleaning performance can be monitored in real-time in order to make sure drilled cuttings generated are efficiently transported to surface. The objective of this paper to present a real time automated model to obtain hole cleaning efficiency and thus effectively adjust parameters as required to improve drilling performance. The process adopts a modified real time carrying capacity indicator. There are many hole cleaning models, methodologies, chemicals and correlations, but majority of these models do not simulate drilling operations sequences and are not dependent on practicality of drilling operations. The developed real time hole cleaning indicator can ensure continuous monitoring and evaluation of hole cleaning performance during drilling operations. The methodology of real time model development is by selecting offset mechanical drilling parameters and drilling fluid parameters where collected, analyzed, tested and validated to model strong hole cleaning efficiency indicator that can extremely participate and facilitate a position in drilling automations and fourth industry revolution. The automated hole cleaning model is utilizing real time sensors of drilling and validate the strongest relationships among the variables. The study, analysis, test and validation of the relationships will reveal the significant parameters that will contribute massively for model development procedures. The model can be run as well by using the real time sensors readings and their inputs to be fed into the developed automated model. The developed model of real time carrying capacity indicator profile will be shown as function of depth, drilling fluid density, flow rate of mud pump or mud pump output, and other important factors will be illustrated by details. The model has been developed and validated in the field of drilling operations to empower the drilling teams for better and understandable monitoring and evaluation of hole cleaning efficiency while performing drilling operations. The real time model can provide a vision for better control of mud additives and that will contribute to mud cost effectiveness. The automated model of hole cleaning efficiency optimized the rate of penetration (ROP) by 50% in well drilling performance as a noticeable and valuable improvement. This optimum improvement saved cost and time of rig and drilling of wells and contributed to accelerate wells’ delivery. The innovative real time model was developed to optimize drilling and operations efficiency by using the surface rig sensors and interpret the downhole measurements and that can lead innovatively to other important hole cleaning indicators and other tactics for better development of downhole measurements models that can participate for optimized drilling efficiency.

Unconventional reservoirs are characterized by their extremely low permeabilities surrounded by huge in-situ stresses. Hydraulic fracturing is a most commonly used stimulation technique to produce from such reservoirs. Due to high in situ stresses, breakdown pressure of the rock can be too difficult to achieve despite of reaching maximum pumping capacity. In this study, a new model is proposed to predict the breakdown pressures of the rock. An extensive experimental study was carried out on different cylindrical specimens and the hydraulic fracturing stimulation was performed with different fracturing fluids. Stimulation was carried out to record the rock breakdown pressure. Different types of fracturing fluids such as slick water, linear gel, cross-linked gels, guar gum, and heavy oil were tested. The experiments were carried out on different types of rock samples such as shales, sandstone, and tight carbonates. An extensive rock mechanical study was conducted to measure the elastic and failure parameters of the rock samples tested. An artificial neural network was used to correlate the breakdown pressure of the rock as a function of fracturing fluids, experimental conditions, and rock properties. Fracturing fluid properties included injection rate and fluid viscosity. Rock properties included were tensile strength, unconfined compressive strength, Young's Modulus, Poisson's ratio, porosity, permeability, and bulk density. In the process of data training, we analyzed and optimized the parameters of the neural network, including activation function, number of hidden layers, number of neurons in each layer, training times, data set division, and obtained the optimal model suitable for prediction of breakdown pressure. With the optimal setting of the neural network, we were successfully able to predict the breakdown pressure of the unconventional formation with an accuracy of 95%. The proposed method can greatly reduce the prediction cost of rock breakdown pressure before the fracturing operation of new wells and provides an optional method for the evaluation of tight oil reservoirs.

Summary The objective of this study is to develop a correlation for transient wellbore-pressure buildup and relate it to the high-integrity pressure-protection system (HIPPS) response time in multiphase flow. The developed correlation gives an accurate estimate of the pressure buildup with time under shut-in conditions. It provides guidelines to set the HIPPS activation pressure considering different reservoir and wellbore parameters to ensure the safety and timely response of HIPPS. The correlation was developed by performing transient simulations to calculate the wellbore-pressure-buildup time under shut-in conditions accounting for different reservoir and wellbore parameters. The input data were gathered from three oil fields with various fluid properties, reservoir pressures, productivity indices (PIs), depths, and shut-in wellhead pressures (SIWHPs). Before feeding the data to the dynamic flow simulator, the reservoir data were tuned to match the well-flow conditions. After completing the transient simulation for every well, the pressure-buildup data as a function of time were collected and used as input to develop a correlation using the nonlinear-regression method. The results of this study show that the most influential parameters on the pressure-buildup behaviors are fluid compressibility, PI, flowing wellhead pressure (FWHP), and well measured depth (MD). In addition, we observed that there is a strong relationship between the fluid compressibility at FWHP condition and the time it takes to pressurize the wellbore to maximum pressure. The higher the fluid compressibility, the longer it takes the system to pressurize. The newly developed correlation provides a guideline for setting the HIPPS activation pressure and ensuring the wells’ safe operation.

Accurate estimation of rock elastic and failure parameters plays a vital role in petroleum, civil and geotechnical engineering applications. During drilling operations, continuous logs of rock elastic and failure parameters are considered very helpful in optimizing geomechanical earth models. Commonly, rock elastic and failure parameters are estimated using well logs and empirical correlations. These are calibrated with rock mechanics laboratory experiments conducted on core samples. However, since these samples are expensive to get and time-consuming to test, artificial intelligence (AI) models based on available petrophysical well logs such as bulk density, compressional wave and shear wave travel times are utilized to predict the static Young's modulus (E static) and the unconfined compressive strength (UCS) – with an emphasis on carbonate rocks. We present two AI techniques in this study: an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The dataset used in this study contains 120 data points obtained from a Middle Eastern carbonate reservoir from which we develop an empirically correlated ANN model to predict E static and an ANFIS model to predict the UCS. A comparison between the UCS, predicted by the proposed ANFIS model, and the published correlations show that the ANFIS model predicted the UCS with less error and with a high coefficient of determination. The error obtained from the ANFIS model was 4.5%, while other correlations resulted in up to 30% error on a published dataset. On the basis of the results obtained, we can say that the developed models will help geomechanical engineers to predict E static and the UCS using well logs without the need to measure them in the laboratory. Thematic collection: This article is part of the Naturally Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/naturally-fractured-reservoirs

An innovative approach in enhancing oil and gas recovery is the in situ heat generation through exothermic chemical reactions using thermochemical fluids. This study presents the kinetics and energ...

Oil and gas industry deals with fluid streams with different ions and concentrations that might cause scale precipitation. The scale precipitation, will thereafter, affect the fluid flow characteristics. Many problems will be raised by the scale deposition that affects the overall petroleum production. This paper aims to develop a non-corrosive acid system with high dissolution efficiency for field complex scales that have sulfates and sulfides minerals. The paper provided a series of lab analysis that covers the compositional analysis for the collected scale sample, and evaluating the developed acid system for compatible and stable properties, dissolution efficiency, and the corrosive impact. A field scale sample that has a composite chemical composition of paraffin, asphaltene, sulfides and sulfates compounds with different weight percentages by employing the diffraction of X-ray technology. Developing the new scale dissolver was achieved by specific compositional study for the organic acids to achieve high dissolution efficiency and low corrosive impact for the field treatment operations. The study results showed the successful scale removal for the developed dissolver at low temperature of 95 and 113 °F for surface treatment jobs. The dissolution efficiency recorded 62 and 71 % for 17 hours at the temperature levels respectively. The fluid showed a stable and compatible performance and has a pH of 12. The corrosion test was conducted without any scale inhibitors and the results showed the low corrosion effect by 0.0028 lbm/ft2. The obtained successful results will help to dissolve such complex field scales, maintain the well equipment, and maintain the petroleum production from scale issues.

Natural gas is one of the main fossil energy resources, and its density is an effective thermodynamic property, which is required in almost every pressure–volume–temperature (PVT) calculation. Conventionally, the density of natural gas is determined from the gas deviation (Z-) factor using an equation of states (EOS). Several models have been developed to estimate the Z-factor utilizing the EOS approach, however, most of these models involve complex calculations and require many input parameters. In this study, an improved natural gas density prediction model is presented using robust machine learning techniques such as artificial neural networks and functional networks. A total of 3800 data points were collected from different published sources covering a wide range of input parameters. Moreover, explicit empirical correlations are also derived that can be used explicitly without the need for any machine learning-based software. The proposed correlations are a function of molecular weight (Mw) of natural gas, pseudo-reduced pressure (Ppr), and pseudo-reduced temperature (Tpr). The proposed correlations can be applied for the gases having Mw between 16 and 129.7 g, Ppr range of 0.02–29.3, and Tpr range 0.of 5–2.7. The prediction of the new correlation was compared against the most common methods for determining the natural gas density. The developed correlation showed better estimation than the common prediction models. The estimation error was reduced by 2% on average using the new correlations, and the coefficient of determination (R2) was 0.98 using the developed correlation.

Green enhanced oil recovery (GEOR) is an environmentally friendly enhanced oil recovery (EOR) process involving the injection of green fluids to improve macroscopic and microscopic sweep efficiencies while boosting tertiary oil production. Carbon nanomaterials such as graphene, carbon nanotube (CNT), and carbon dots have gained interest for their superior ability to increase oil recovery. These particles have been successfully tested in EOR, although they are expensive and do not extend to GEOR. In addition, the application of carbon particles in the GEOR method is not well understood yet, requiring thorough documentation. The goals of this work are to develop carbon nanoparticles from biomass and explore their role in GEOR. The carbon nanoparticles were prepared from date leaves, which are inexpensive biomass, through pyrolysis and ball-milling methods. The synthesized carbon nanomaterials were characterized using the standard process. Three formulations of functionalized and non-functionalized date-leaf carbon nanoparticle (DLCNP) solutions were chosen for core floods based on phase behavior and interfacial tension (IFT) properties to examine their potential for smart water and green chemical flooding. The carboxylated DLCNP was mixed with distilled water in the first formulation to be tested for smart water flood in the sandstone core. After water flooding, this formulation recovered 9% incremental oil of the oil initially in place. In contrast, non-functionalized DLCNP formulated with (the biodegradable) surfactant alkyl polyglycoside and NaCl produced 18% more tertiary oil than the CNT. This work thus provides new green chemical agents and formulations for EOR applications so that oil can be produced more economically and sustainably.

Emulsification involving dispersion of bitumen droplets in a continuous aqueous phase (as oil-in-water (O/W) emulsion), is an efficient method of reducing the viscosity. The objective of this research is to harness the potential of insitu emulsification for production of bitumen to improve energy efficiency. Thus, O/W emulsion was prepared using poly vinyl alcohol (PVA) surfactant (with NaOH and ethanol additivities) at different ratios of bitumen: PVA solution viz. 70:30 (RX1), 55:45 (RX2), 40:60 (RX3). The data was incorporated in computational fluid dynamics (CFD) analysis to obtain emulsification reaction parameters at different temperatures (30–150 °C). Subsequently, numerical simulation considering insitu formation of O/W emulsion was performed at different injection temperatures (50, 100, and 150 °C). The results were compared with those of conventional steam injection at 215 °C. Significant viscosity reduction of bitumen was obtained from emulsification experiments. From numerical simulation, the proposed method resulted in higher oil: steam ratio (OSR) compared with steam injection method. Ultimately, with reference to steam injection (thermal efficiency, Δ E e f f = 0.02 m3/GJ; net bitumen production = 692 m3), the most promising operation is the production from RX3, at 150 °C, with thermal efficiency, Δ E e f f = 0.04 m3/GJ, and 649 m3 net bitumen production.

The successful design of an acid fracture job requires accurate prediction of fractured well productivity. Productivity estimation demands knowledge of both the acid penetration length and conductivity distribution for the given reservoir. The literature includes several models developed to predict the conductivity of acid fractured rock. The most popular is empirical and based on measuring the conductivity of 25 acid fracture experiments. The present research provides empirical models utilizing machine learning techniques and incorporating 97 experiments and 563 datapoints. We conducted an extensive literature review to collect the published data on acid fracture experiments. The objective of such experiments is to measure conductivity at different formation closure stresses while considering field conditions. We used several data preprocessing techniques to clean the data, fill in missing values, exclude outliers and failed experiments, and standardize the dataset. Regularization was employed to eliminate features that didn't contribute to accurate prediction. Feature engineering was used to construct new features from our dataset. We began by measuring the correlations between features to better understand the data. We then built various machine learning models to predict acid fracture conductivity. It has been observed that developing one universal empirical correlation often results in significant errors in conductivity estimation because different rock types result in different etching patterns that cannot be explained by a single correlation. For instance, the channeling etching pattern is mostly observed in limestone formations, while a roughness pattern is seen in dolomite and chalk rock. Moreover, the conductivities of etching patterns formed in chalk, dolomite, and limestone formations behave differently. We built machine learning classification techniques to determine the most likely etching patterns (e.g., channeling, roughness). A linear regression-based model was then developed as a baseline for comparison with other models generated through ridge regression. We evaluated the performances of our models using well-known metrics such as accuracy, precision, recall, mean squared error, and correlation coefficients. We also employed cross-validation to avoid over-fitting, finding that certain features were the most important in predicting acid fracture conductivity. Detailed empirical conductivity correlations and models were developed in this work for three carbonate rock types. Previously, a single empirical model has often been employed to estimate acid fracture conductivity or, at best, a model has been developed for a particular rock type. Most models have not considered the impact of etching patterns on conductivity, which was found to be significant in limestone.

In the dynamic approach, production data including the flowing wellhead or bottomhole pressures are used to conduct the material balance analysis to determine the gas in place at any time during the life of well. However, the procedure did not extend to identify the well health issues such as permeability variation. The objective of this research is to develop a simplified mathematical workflow to identify and verify permeability variation using flowing gas material balance. In this approach, a new form of flowing gas material balance equation is derived by adding permeability variation term. In this new mathematical workflow, linear and exponential curve fitting tools along with the flowing material balance analysis are used to predict well expected behavior. The prediction is then compared with initial well production behavior. The deviation of the prediction and the expected production behavior formed the basis of permeability variation. The component of the model is tested with known data and then the total model is verified with known values. After that, the workflow is applied in the gas well. The new model prediction is validated using field data and the prediction compares quite well. The well health diagnostics are successfully compared with known well issues from an active gas field. Results presented in this paper will show that the novel workflow is able to accurately predict permeability variations in both constant and variable production rates. In addition, the new workflow enables the production engineers to send an alarm to take corrective measure about the issue and to diagnose production issues like permeability variation.

Among the various enhanced oil recovery (EOR) processes, CO2 injection has been widely utilized for oil displacement in EOR. Unfortunately, gas injection suffers from gravity override and high mobility, which reduces the sweep efficiency and oil recovery. Foams can counter these problems by reducing gas mobility, which significantly increases the macroscopic sweep efficiency and results in higher recovery. Nevertheless, CO2 is unable to generate foam or strong foam above its supercritical conditions (for CO2, 1100 psi at 31.1 °C), and most of the reservoirs exist at higher temperatures and pressure than CO2 supercritical conditions. The formation of strong CO2 foam becomes more difficult with an increase in pressure and temperature above its supercritical conditions and exacerbated CO2-foam properties. These difficulties can be overcome by replacing a portion of CO2 with N2 because a mixture of N2 and CO2 gases can generate foam or strong foam above CO2 supercritical conditions. Although many researchers have investigated EOR by using CO2 or N2 foam separately, the performance of mixed CO2/N2 foam on EOR has not been investigated. This study provides a solution to generate CO2 foam above its supercritical conditions by replacing part of CO2 with N2 (mixed CO2/N2 foam). The mixed foam not only generates strong foam above CO2 supercritical conditions but also remarkably increases the oil recovery. This solution overcomes the difficulties associated with the formation of CO2 foam at HPHT conditions enabling the use of the CO2-foam system for effective EOR and other applications of CO2 foam such as conformance control.

Several publications by authors in the field of petrochemical engineering have examined the use of chemically enhanced oil recovery (CEOR) technology, with a specific interest in polymer flooding. Most observations thus far in this field have been based on the application of certain chemicals and/or physical properties within this technique regarding the production of 50–60% trapped (residual) oil in a reservoir. However, there is limited information within the literature about the combined effects of this process on whole properties (physical and chemical). Accordingly, in this work, we present a clear distinction between the use of xanthan gum (XG) and hydrolyzed polyacrylamide (HPAM) as a polymer flood, serving as a background for future studies. XG and HPAM have been chosen for this study because of their wide acceptance in relation to EOR processes. To this degree, the combined effect of a polymer’s rheological properties, retention, inaccessible pore volume (PV), permeability reduction, polymer mobility, the effects of salinity and temperature, and costs are all investigated in this study. Further, the generic screening and design criteria for a polymer flood with emphasis on XG and HPAM are explained. Finally, a comparative study on the conditions for laboratory (experimental), pilot-scale, and field-scale application is presented.

Different additives such as barite, calcium carbonate, hematite, and ilmenite having high-density and fine solid materials are used to increase the density of drilling fluids. However, some of the weighting additives can cause some serious drilling problems such as barite (particle settling, formation damage, erosion, and insoluble filter cake). In this study and for the first time, anhydrite (calcium sulfate) is used as a weighting additive in the drilling fluids. Several laboratory experiments such as density, rheology, fluid loss, resistivity, and pH were carried out to assess the performance of calcium sulfate as a weighting additive in the drilling fluids. The performance of calcium sulfate as a weighting additive was compared with the commonly used weight enhancing additive calcium carbonate. The results showed that calcium sulfate has higher solubility than calcium carbonate. The fluid loss test showed that both additives lost the same volume of fluid and created the same thickness of filter cake; however, the solubility of calcium sulfate-based filter cake with organic and inorganic acids was higher compared with other weighting materials. Calcium sulfate-based filter cake was completely dissolved using a new formulation that consists of glutamic-diacetic acid (GLDA) chelating agent and potassium carbonate as a convertor. The removal efficiency after 10 h reached 100% in 20 wt% GLDA and 10 wt% potassium carbonate solution at 100 °C.

Synthetic well-log generation using artificial intelligence tools is presented as a robust solution when the logging data are not available or partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. It is measured in the field using density log tool either while drilling by logging while drilling technique (LWD) or mostly by wireline logging after the formations are drilled because of the operational limitations during the drilling process. Therefore the objective of this study is to develop a predictive tool for estimating RHOB while drilling using artificial neural networks (ANN) and Adaptive network-based fuzzy interference systems (ANFIS). The proposed models used the drilling mechanical parameters as feeding inputs and the conventional RHOB log-data as an output. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), stand-pipe pressure (SPP) and rotating speed (RPM), are usually measured while drilling and their responses vary with different formations. A dataset of 2400 actual data points obtained from horizontal well in the Middle East is used for building the proposed models. The obtained dataset is divided into 70/30 ratios for training and testing the model respectively. The optimized ANN-based model outperformed the ANFIS-based model with correlation coefficient (R) of 0.95 and average absolute percentage error (AAPE) of 0.72 % between the predicted and the measured RHOB compared to R of 0.93 and AAPE of 0.81 % for the ANFIS-based model. These results demonstrated the reliability of the developed ANN model to predict the RHOB while drilling based on the drilling mechanical parameters. Afterwards, the ANN-based model is validated using unseen data from another well within the same field. The validation process yielded AAPE of 0.5 % between the predicted and the actual RHOB values which confirmed the robustness of the developed model as an effective predictive tool.