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The drilling industry has evolved significantly over the years, with new technologies making the process more efficient and effective. One of the most crucial issues of drilling is borehole cleaning, which entails removing drill cuttings and keeping the borehole clean. Inadequate borehole cleaning can lead to drilling problems such as stuck pipes, poor cementing, and formation damage. Real-time drilling evaluation has seen significant improvements, allowing drilling engineers to monitor the drilling process and make adjustments accordingly. This paper introduces a novel real-time borehole cleaning performance evaluation model based on the transport index (TIm). The novel TIm model offers a real-time indication of borehole cleaning efficiency. The novel model was field-tested and validated for three wells, demonstrating its ability to determine borehole cleaning efficiency in typical drilling operations. Using TIm in Well-A led to a 56% increase in the rate of penetration (ROP) and a 44% reduction in torque. Moreover, the efficient borehole cleaning obtained through the use of TIm played a significant role in improving drilling efficiency and preventing stuck pipes incidents. The TIm model was also able to identify borehole cleaning efficiency during a stuck pipe issue, highlighting its potential use as a tool for optimizing drilling performance.

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.

The hydrostatic pressure exerted during the drilling operation is controlled by adding a weighting agent into drilling fluids. Various weighting materials such as barite, calcium carbonate, hematite, and ilmenite are used to increase the density of drilling fluids. Some weighting additives can cause serious drilling problems, including particle settling, formation damage, erosion, and insoluble filters. In this study, anhydrite (calcium sulfate) is used as a weighting additive in the oil-based drilling fluid (OBDF). Anhydrite is an abundantly available resource used in the preparation of desiccant, plaster of Paris, and Stucco. Anhydrite application in drilling fluids is discouraged because of its filter cake removal issue. This study investigated anhydrite (anhydrous CaSO4) as a weighting agent and its filter cake removal procedure for OBDFs. The anhydrite performance as a weighting agent in OBDFs was evaluated by conducting several laboratory experiments such as density, rheology, fluid loss, and electrical stability and compared with that of commonly used weighting materials (barite, calcium carbonate, and hematite). The anhydrite was mixed in three different concentrations (62, 124, and 175 ppb) in a base-drilling fluid. The results showed that calcium sulfate enhanced rheological parameters such as plastic viscosity, yield point, apparent viscosity, and gel strength. CaSO4 reduced the fluid loss and provided better control over the fluid loss than other tested weighting materials tested at the same concentration of 124 ppb. Similarly, the emulsion stability was decreased with the increase in the amount of calcium sulfate in the OBDF. The calcium sulfate filter cake can be removed easily from the wellbore with an efficiency of 83 to 91% in single-stage and multistage removal processes, respectively using the newly developed formulation consisting of 20 wt % potassium salt of glutamic acid-N,N-diacetic acid (K4GLDA) as a chelating agent, 6 wt % potassium carbonate, and 10% ethylene glycol monobutyl ether. The introduction of anhydrite as a weighting agent can be more beneficial for both academia and industry.

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.

Asphaltene precipitation and deposition have been a formation damage problem for decades, with the most devastating effects being wettability alteration and permeability impairment. To this effect, a critical look into the laboratory studies and models developed to quantify/predict permeability and wettability alterations are reviewed, stating their assumptions and limitations. For wettability alterations, the mechanism is predominantly surface adsorption, which is controlled by the asphaltene contacting minerals as they control the surface chemistry, charge, and electrochemical interactions. The most promising wettability alteration evaluation techniques are nuclear magnetic resonance, ζ potential, and the use of high-resolution microscopy. The integration of such techniques, which is still missing, would reinforce the understanding of asphaltene interaction with rock minerals (especially clays), which holds the key to developing a strategy for modeling wettability alteration. With regard to permeability impairment, surface deposition, pore plugging, and fine migration have been identified as the dominant mechanisms with several models reporting the simultaneous existence of multiple mechanisms. Existing experimental findings showed that asphaltene deposition is non-uniform due to mineral distribution which further complicates the modeling process. It also remains a challenge to separate changes due to adsorption (wettability changes) from those due to pore size reduction (permeability impairment).

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.

Reservoir rock wettability has been linked to the adsorption of crude fractions on the rock, with much attention often paid to the bulk mineralogy rather than contacting minerals. Crude oil is contacted by different minerals that contribute to rock wettability. The clay mineral effect on wettability alterations is examined using the mineral surface charge. Also, the pH change effect due to well operations was investigated. Clay mineral surface charge was examined using zeta potential computed from the particle electrophoretic mobility. Clay minerals considered in this study include kaolinite, montmorillonite, illite, and chlorite. Results reveal that the clay mineral charge development is controlled by adsorption of ionic species and double layer collapse. Also, clay mineral surface charge considered in this study shows that their surfaces become more conducive for the adsorption of hydrocarbon components due to the presence of salts. The salt effect is greater in the following order: NaHCO3 < Na2SO4 < NaCl < MgCl2 < CaCl2. Furthermore, different well operations induce pH environments that change the clay mineral surface charge. This change results in adsorption prone surfaces and with reservoir rock made up of different minerals, and the effect of contacting minerals is critical as shown in our findings. This is due to the contacting mineral control wettability rather than the bulk mineralogy.

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.

Asphaltene adsorption and deposition onto rock surfaces are predominantly the cause of wettability and permeability alterations which result in well productivity losses. These alterations can be induced by rock-fluid interactions which are affected by well operations such as acidizing, stimulation, gas injections, and so forth. Iron minerals are found abundantly in sandstone reservoir formations and pose a problem by precipitation and adsorption of polar crude components. This is due to rock-fluid interactions, which are dependent on reservoir pH; thus, this research work studied the surface charge development of pyrite, magnetite, and hematite. To ascertain conditions that will result in iron mineral precipitation and adsorption of asphaltene on iron mineral surfaces, zeta potential measurement was carried out. This is to determine the charge and colloidal stability of the iron mineral samples across wide pH values. Experimental results show that the charge development of iron minerals is controlled by mineral dissolution, the formation of complexes, adsorption of ions on the mineral surface, and the collapse of the double layer. The findings provide insights into the implications of iron mineral contacting crude oil in reservoir formations and how they contribute to wettability alterations due to different well operations.

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.

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.

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.

Acid-fracturing operations are mainly applied in tight carbonate formations to create a highly conductive path. Estimating the conductivity of a hydraulic fracture is essential for predicting the fractured well productivity. Several models were developed previously to estimate the conductivity of acid-fractured rocks. In this research, machine learning methods were applied to 560 acid fracture experimental datapoints to develop several conductivity correlations that honor the rock types and etching patterns. Developing one universal correlation often results in significant error. To develop conductivity correlations, various data preprocessing methods were applied to remove the outliers and failed experiments. Features that did not contribute to precise predictions were removed through regularization. A machine learning classifier was built to predict the etching pattern based on the input data. We generated a multivariate linear regression model and compared it with other models generated through ridge regression. In addition to that, artificial neural network-based model was proposed to predict the fracture conductivity of several carbonate rocks such as dolomite, chalk, and limestone. The performance of the developed models was assessed using well-known metrics such as precision, accuracy, mean squared error, recall, and correlation coefficients. Cross-validation was also employed to assure accuracy and avoid overfitting. The classifier accuracy was 93%, while the regression model resulted in a relatively high correlation coefficient.

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.

This article focuses on the flow assurance of waxy crude oil using an environmentally benign and cost-effective approach involving thermochemical reaction. The study incorporates experimental and simulation works to evaluate heat and pressure generation potentials and heat transfer efficiency of the thermochemical fluids. Experimental results reveal that at the concentration (1 M) of thermochemical fluid (TCF) ranging between 14 and 33% v/wt of the waxy oil, sufficient heat could be generated to raise the temperature of the oil significantly above the pour point (48 °C). In addition, from the bench-top treatment of a damaged tubing, it was observed that more than 95% of the deposited wax could be removed using the thermochemical solutions. Subsequently, a large-scale application of the technology in a long-distance flow assurance of waxy crude oil was confirmed through process simulation. Ultimately, the simulation results revealed the capacity of the method to improve the temperature and pressure profiles of the pipe flow system, and most significantly, to remove wax deposition up to 98%.

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.