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This manuscript aimed to propose a new approach in estimating the cutting transport ratio through a relationship between equivalent circulating density (ECD) and drilled solid concentration from different formations in a nearly vertical well. A laboratory test utilising drilled cuttings from North Kuwait Field was conducted to simulate drilled solids-laden mud for accomplishing this purpose. The drilled solids were taken from four formations, namely Radhuma, Tayarat, Hartha and Sadi. The rheological behaviour of the drilled solids-laden mud was conducted according to API Recommended Practice 13B. In addition to laboratory tests, a database was established from drilled solids produced in the three wells of the North Kuwait field. As a result of the laboratory tests and actual field data, new empirical equation between drilled solid concentration and equivalent circulating density was proposed. ECD from laboratory works matched with the ECD field at an R2 value of 0.93, proving the data’s reliability. This empirical equation can be used to calculate the cutting transport ratio and later can be used to optimise drilling operations.

The oil-based mud is preferred to drill highly technical and challenging formations due to its superior performance. However, the inadequate chemical and thermal stability of conventional additives have greatly influenced the performance of oil-based mud at high-temperature conditions. Therefore, it is critical to design an oil-based mud with additives that withstand and improve its performance at high-temperature conditions. The nanoparticles have emerged as an alternative to the conventional additives that can significantly enhance the rheological and filtration characteristics of oil-based mud at high-temperature conditions. In this research study, a novel formulation of OBM enhanced with GNP is formulated, and its performance at high-temperature conditions is investigated. An extensive experimental study has been performed to study the effect of graphene nanoplatelets on the rheological and filtration properties along with flow behaviour, viscoelastic properties, electrical stability and barite sagging of oil-based mud at high temperatures. The graphene nanoplatelets are characterised to ascertain their purity and morphology. The result shows that the graphene nanoplatelets exhibited efficient performance and improved the rheological and filtration properties of oil-based mud. The plastic viscosity and yield point are improved by 11% and 42%, with a concentration of 0.3 ppb. Similarly, the gel strength and barite sagging tendency are enhanced by 14% and 2%, respectively. The filtration loss is also significantly decreased by up to 62% and 46%, with 0.5 ppb concentration at 100 and 120 °C. The addition of GNP results in the formation of a thin mud cake compared to the base mud sample. The rheological modelling recommends the shear-thinning behaviour of oil-based mud (n Graphical abstract

Energy resource extraction is getting more difficult and using the enhanced and advanced level of technology for oil production is getting more common. Steam-assisted gravity drainage (SAGD) and Vapor extraction (VAPEX) are two favorable methods of EOR in heavy oil reservoirs. However, there are some obscure points about these methods, like optimum distances between wells and the overall cost of each method. The main objectives of this project were to find the best distance of wells in these types of EOR methods and to enhance the production of heavy oil reservoirs by SAGD and VAPEX methods, taking into consideration of the economic aspects of this project. In this project, SAGD and VAPEX methods were modeled to increase the production rate. Each stage of the Enhance Oil Recovery (EOR) was simulated and modified through many scenarios in terms of the injection patterns, production & injection wells’ locations as well as the adjustment of different injection steam injection and solvent injection. This project analyzed the SAGD method in the shallow reservoir (RF of 69.8%), and in the deep reservoir (RF of 38%). In addition, the maximum allowable depth to apply the SAGD method was found in this study (3500ft). Based on the calculation from the software, it was observed that the heat loss for this reservoir was around 350°F. The cumulative oil production of the SAGD method in the shallow reservoir was 3.87×10^6 bbl and the deep reservoir was2.1×10^6 bbl, and it for the VAPEX method at the shallow reservoir was 2.74×10^6 bbl, and at deep reservoir around 2.64×10^6 bbl, based on the results of each method the VAPEX method remain with the same quality at both reservoirs, however, the SAGD has the lower production in the deep reservoir which means this method is not suitable for the deep reservoir. Furthermore, the profit of each method was calculated; the profit of SAGD and VAPEX were (190.97 MM$ & 168.97 MM$) at the shallow reservoir, and (72.97 MM$ 162.32 MM$) at the deep reservoir for each process. Finally, the spacing between injection and production wells was obtained at different distances and the best distance was 30 m.

Widespread applications of raw materials in building construction with population generation have steadily increased the demand for fresh raw materials and provoked environmental pollution. Development of sustainable building materials by recycling various solid waste could be a viable approach to reduce the problems arising from generating solid waste besides providing an alternative raw material for building construction. The present study demonstrates the development of polymeric composite materials as floor tile using different waste plastics, includes lowdensity polyethylene, high-density polyethylene, polyethylene terephthalate, and polypropylene as matrices with incorporation rice husk ash and sand as fillers. The workability of the developed materials was verified through experimental evaluations of physical, mechanical, and tribological properties. The minimum values of water absorption and abrasive wear were found to be 0.0397 % and 0.03267 (cm3 ) for the composites LDPE50PET20S30 and LD50S50, respectively. However, the composites HD50S50 resulted in an optimum mechanical strength with compressive strength and flexural strength of 46.2 and 6.24 (N/mm2 ), respectively. It was observed that the workability of the composites improves with the incorporation of sand particles. The scope of the present study relies on the development of methods and techniques for developing sustainable building materials through recycling of solid waste along with its characterization which provides an easy reference for solid waste processing towards sustainability.

Geopolymer cement has significance in several engineering applications due to improved physical and chemical characteristics compared to ordinary portland cement (OPC). However, the emphasis on petroleum industry for cementing is not well recorded under CO2 exposure. This experimental research aims to investigate the impact of CO2-saturated brine on geopolymer-based and OPC-based cement under elevated temperature. Fly ash-based alkali-activated cement is prepared, and cement slurry is cured at two different conditions (17.23 MPa/60 °C and 24.13 MPa/130 °C). The slurry is then submerged into CO2-saturated brine using autoclave chamber at supercritical CO2 conditions for 24, 72 and 96 h. Microstructure properties are characterized using SEM, XRF, XRD, and IR. Compression strength is experimentally tested on cubical cement samples. A comparative analysis of fly ash geopolymer cement and OPC at varying conditions exhibits that microstructure and compressive strength of geopolymer cement show better performance. It signifies the potential of fly ash as a binder in sequestration cement for CO2 injection wells. This research suggests that the concentration of CO2 has a minor influence on the degree of carbonation. However, the temperature is found to be a critical factor for microstructure and mechanical properties reduction in OPC in a CO2-rich environment.

The minimum miscibility pressure (MMP) CO2 injection performs in enhanced oil recovery (EOR) method to increase sweeping efficiency and reduce oil/CO2 interfacial tension (IFT) also advantageous to the environmental in term of gas emissions for carbon capturing. There are several methods to achieve reliable value of MMP such as slim tube test, raising bubble apparatus, vanishing IFT, swelling test, and visual observation. However, these methods have certain limitations, which leads to the development of new techniques for a wide range of applications. In this paper, a rapid method that integrated IFT test with visual observation was investigated. Based on the test, the pressure is plotted against the IFT to predict the MMP for temperature 60 °C and 66 °C. In the meantime, visual observation during the test is also conducted to identify the occurrence of miscibility. The combination of both methods may provide much faster MMP prediction because the test consumes a small amount of hydrocarbon samples. The outcomes of this research clearly suggest that the MMP values resulted from IFT test and visual observation considerably agree with each other.

Wellbore stability in shale is a recurring crisis during oil and gas well drilling. The adsorption of water and ions from drilling fluid by shale, which causes clay swelling, is the primary cause of wellbore instability. Nanomaterials have been a subject of interest in recent years to be an effective shale inhibitor in drilling fluid, intending to minimize clay swelling. This article presents a comprehensive review of the current progress of nanoparticle role in water-based drilling fluid with regards to wellbore stability, reviewing the experimental methods, the effect of nanoparticles in drilling fluid, the mechanism of shale stability and the outlook for future research. This paper employed a systematic review methodology to highlight the progress of nanoparticle water-based drilling fluids in recent years. Previous studies indicated the current trend for drilling fluid additives was nanoparticles modified with surfactants and polymers, which minimize colloidal stability issues and enhance shale stability. A review of experimental methods showed that the pressure transmission test benefits shale stability assessment under reservoir conditions. Parametric analysis of nanoparticles showed that parameters such as concentration and size directly affected the shale stability even in high salinity solution. However, there is a lack of studies on nanoparticle types, with silica nanoparticles being the most popular among researchers. Nanoparticles enhance shale stability via physical plugging, chemical inhibition, and electrostatic interactions between surface charges. To better comprehend the influence of nanoparticles on shale stabilization, it is necessary to evaluate a wider range of nanoparticle types using the proper experimental techniques.

With the increasing of water injection activities especially for marginal or stranded fields, the well pair analysis in routine water injection surveillance is crucial to understand the reservoir performance and identify opportunities to improve the ultimate oil recovery. This article aims to propose an alternative technique to evaluate the communication strength between injector – producer well pairs based on statistical and machine learning algorithms. The proposed technique is applied to an offshore water injection field located in the North Sea from open-source data. A novel formulation to quantify the communication strength coefficient for an injector – producer well pair was derived from the Spearman's rank correlation coefficient. The calculation is controlled with injection/production rates pattern for each well pair. Subsequently, multivariate parametric regression is performed to model the communication strength coefficient as a function of injector – producer spacing, injection pattern (dip angle), and reservoir permeability-thickness. Monte Carlo technique is then applied to simulate 100 cases prepared using the uniform probability distribution. Afterward, the communication strength for all the well pairs in the field is classified based on K-means clustering. To identify opportunities to improve the effectiveness of water injection operation, random forest and support vector machine algorithms are used to evaluate the effect of the reservoir and operational parameters on the communication strength of the injector – producer well pair. It is identified that the communication strength for all the well pairs in the field varying from limited, intermediate, and good communication. Good communication strength shows the correlation coefficient of more than 0.50 which indicates there is a good correlation between injection and production rates pattern. It is also observed that reservoir permeability-thickness is the most variable importance that affects the communication strength between injector and producer well pair. It is followed by the injector-producer spacing and reservoir dip angle. The optimum condition has been identified to formulate the screening criteria in order to obtain the good communication strength between injector and producer well pair. This result help in identifying the producer with limited communication strength with the existing injector and low production rate to be converted as the injector well. Unlike reservoir simulation which is a very expensive and time-consuming process, this work provides a quick and inexpensive alternative to evaluate the communication strength of injector-producer well pair from widely available measurements of production and injection rates at existing wells. Application of this novel workflow provides insight for better decision-making and can be a prudent complementary tool to quantify the effectiveness of the water injection operation and identify opportunities.

A complete overview of the rheology and filtration properties of drilling fluids is essential to ensure an efficient transport process with minimized fluid loss. Silica nanoparticle is an excellent additive for rheology and filtration properties enhancement. Existing correlations are not available for nano-SiO2-water-based drilling fluid that can extensively quantify the rheology or filtration loss of nanofluids. Thus, two data-driven machine learning approaches are proposed for prediction, i.e., artificial neural network (ANN) and least square support vector machine (LSSVM). Parameters involved in the prediction of shear stress are SiO2 concentration, temperature, and shear rate, whereas SiO2 nanoparticle concentration, temperature, and time are the inputs to simulate filtration volume. A feed-forward multilayer perceptron is constructed and optimized using the Levenberg–Marquardt learning algorithm. The parameters for the LSSVM are optimized using couple simulated annealing (CSA). The performance of each model is evaluated based on several statistical parameters. The predicted results achieved R2 (coefficient of determination) value higher than 0.99 and mean absolute error (MAE) and mean absolute percentage error (MAPE) value below 7% for both the models. The developed models are further validated with experimental data that reveals an excellent agreement between predicted and experimental data.

Accurate prediction of downhole pressure differential (surge/swab pressure gradient) in the eccentric annulus of ultra-deep wells during tripping operation is a necessity to optimize well geometry, reduction of drilling anomalies, and prevention of hazardous drilling accidents. Therefore, a new predictive model is developed to forecast surge/swab pressure gradient by using feed-forward and backpropagation deep neural networks (FFBP-DNN). A theoretical-based model is developed that follows the physical and mechanical aspects of surge/swab pressure generation in eccentric annulus during tripping operation. The data generated from this model, field data, and experimental data are used to train and test the FFBP-DNN networks. The network is developed used Keras’s deep learning framework. After testing the models, the most optimal arrangement of FFBP-DNN is the ReLU algorithm as an activation function, 4-hidden layers, the learning rate of 0.003, and 2300 of training numbers. The optimum FFBP-DNN model is validated by comparing it with field data (Wells K 470 and K 480, North Sea). It shows an excellent argument between predicted data and field data with an error range of ±7.68 %.

International audience; Recent advancements in thermo-fluid technology assisted with highly thermal conductive nanomaterials have shown assuring outcomes. It is also proven that thermal conductivity alone cannot define the overall heat transfer characteristics, and the viscous properties are equally significant towards thermal management. Therefore, this research involves investigating the rheological behavior of hybrid nanosuspensions containing high thermally conductive diamond and graphene nanoplatelets (1:1). These nanomaterials are dispersed in mineral oil using a two-step technique. Hybrid nanofluids' stability is achieved using a non-ionic stabilizer Span85, exhibiting no sedimentation for a minimum of five months. Nanomaterial characterizations are performed to study morphology, purity, and chemical analysis. The flow behavior of hybrid nanosuspensions is investigated at varying nanomaterial mass concentrations (0–2%), temperatures (298.15–338.15 K), and shear rates (1–2000 s−1). Hybrid nanofluids exhibit shear-thinning behavior, which is also correlated with the Ostwald-de-Waele model. The temperature-viscosity relationship is well predicted using the Vogel-Fulcher-Tammann model. Hybrid nanofluids show a maximum enhancement of 35% viscosity at 2% concentration. A generalized two-variable correlation is used to express viscosity as a function of temperature and nanofluid concentration with an excellent agreement. Three different machine learning methods, i.e., Artificial Neural Network (ANN), Gradient Boosting Machine (GBM), and Random Forest (RF) algorithms are also introduced to predict the viscosity of hybrid nanofluids based on the three input parameters (temperature, concentration, and shear rate). The parity plots conclude that all algorithms can predict big-data viscous behavior with high precision. © 2021 Elsevier B.V.

Ordinary and partial differential equations play a significant role across various energy domain as they aid in approximating solution for complex mathematical problems. Drilling optimization and reservoir simulation are some common application that takes the form of differential equations and are dominated by their respective governing equations. Approximating the solution of such mathematical problems requires a fast and reliable methodology. However, the computational complexity increases with the dimension for the classical numerical techniques and the quality of the result is dependent upon the discretization and sampling methods of the subspace. Recent advances in deep learning techniques, based on universal approximation theorem of neural network seems promising to tackle the high dimensional problem. The solution provided by deep learning for a differential equation is in a closed analytical form which is differentiable and could be used in any subsequent computation. In the present study, the solution for the initial condition and boundary value problems in ordinary and partial differential equation by deep learning method have been analyzed. The propsed algorithm could be valuable aid for analyzing the fluid flow and reservoir simulation in an effective manner.

Surge/swab pressure is a crucial parameter that provokes well-control problems such as fluid loss, fractured formations, fluid influx, and kick. Thus, a precise estimation of differential pressure is required to evade any unforeseen drilling difficulties. The existing predictive models are based on narrow-slot approximation methods and consider the effect of drilling string axial movement on downhole pressure surges. However, it ignores the impact on the boundaries of the annular fluid velocity zone by the tripping velocity. In this research, a simplified model is developed using the flow velocity profile generated in the annulus by the tripping operation and the concentric annular Couette fluid flow phenomena for power-law fluid. A comparative study is performed with the existing analytical models and the experimental data to validate the developed model. The obtained results are convincingly in good agreement with the analytical and experimental data. A parametric study is performed to identify the effect of various parameters on surge/swab pressure. It is found that the diameter ratio has a significant impact on pressure differential with the increase in the tripping velocity. The fluid behavior index exhibits a considerable effect, and fluid consistency index shows a minor effect on the surge pressure gradient. The simplified developed model requires less numerical analysis to determine the outcomes for varying industrial applications, especially petroleum drilling operations.

Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel&ndash, Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

PurposeThe purpose of this paper is to investigate the resistivity of geopolymer cement with nano-silica additive toward acid exposure for oil well cement application.Design/methodology/approachAn experimental study was conducted to assess the acid resistance of fly ash-based geopolymer cement with nano-silica additive at a concentration of 0 and 1 wt.% to understand its effect on the strength and microstructural development. Geopolymer cement of Class C fly ash and API Class G cement were used. The alkaline activator was prepared by mixing the proportion of sodium hydroxide (NaOH) solutions of 8 M and sodium silicate (Na2SiO3) using ratio of 1:2.5 by weight. After casting, the specimens were subjected to elevated curing condition at 3,500 psi and 130°C for 24 h. Durability of cement samples was assessed by immersing them in 15 wt.% of hydrochloric acid and 15 wt.% sulfuric acid for a period of 14 days. Evaluation of its resistance in terms of compressive strength and microstructural behavior were carried out by using ELE ADR 3000 and SEM, respectively.FindingsThe paper shows that geopolymer cement with 1 wt.% addition of nano-silica were highly resistant to sulfuric and hydrochloric acid. The strength increase was contributed by the densification of the microstructure with the addition of nano-silica.Originality/valueThis paper investigates the mechanical property and microstructure behavior of emerging geopolymer cement due to hydrochloric and sulfuric acids exposure. The results provide potential application of fly ash-based geopolymer cement as oil well cementing.

Purpose The purpose of this paper is to investigate the mechanical properties changing of geopolymer cement under different brine salinity. Design/methodology/approach Geopolymer Cement of Class F Fly Ash and Class G Cement slurries were prepared according to API RP 10B. The optimum alkaline activator/cement and water/cement ratio of 0.44 was used for geopolymer and Class G cement samples, respectively. The alkaline activator was prepared by mixing the proportion of Sodium Hydroxide (NaOH) solutions of 8 M and Sodium Silicate (Na2SiO3) using ratio of 1:2.5 by weight. The slurries were cured for 24 hours at 130oC and 3,000 psi in HPHT Curing Chamber followed by coring process. Both cement sample were immersed in brine water salinity up to 28 days with different brine salinity up to 30 per cent of NaCl. The mechanical properties were investigated using OYO Sonic Viewer-SX and Uniaxial Compressive Strength. The surfaces of the cement samples were extracted for Scanning Electron Microscope (SEM) and EDS tests to evaluate the morphology and chemical compositions of the cured samples. Findings The paper shows that geopolymer samples experiences strength reduction in brine water but the reduction rate of geopolymer is about half of the Ordinary Portland cement based oil well cement. The finding was also verified by SEM and EDS result. Originality/value This paper investigates the mechanical property changes of emerging geopolymer cement due to different water salinity. The results provide potential application of geopolymer cement for oil well cementing.

Acidizing is a widely used technique to stimulate an oil well in order to enhance its production. When portland cement is placed in contact with acid fluids, it will dissolve and, at some time, will be fully degraded because the acids have reacted with calcium silicate hydrate which resulted in the breaking up of the cement intergranular structure. This in turn will give rise to well integrity issue. Instead of introducing a new acid type, which may not be as effective toward reservoir formation, researchers have come up with a new cementing material, namely geopolymer cement. Literature has shown that geopolymer cement is unreactive toward conventional acids. In addition, its strength may be improved by introducing Nano-silica in the admixture. However, the studies were conducted at ambient condition. The objective of this work is to investigate geopolymerization mechanism and microstructure behavior of fly ash-based geopolymer cement with Nano-silica admixture. Two cement slurries, geopolymer and Class G OPC, were prepared in accordance with API RP-10B. The slurries were cured for 24 h at 130 $${^{\circ }}$$ C and 20.69 MPa at the HPHT curing chamber before being exposed to 15 wt% acid solution for 14 days. The reaction mechanism of the cement samples was investigated by using FTIR and XRD analyses. The results were further validated by carrying out SEM and EDS tests to evaluate the microstructure behavior and chemical compositions of the cured samples. Results show that fly ash-based geopolymer cement with 1 wt% of Nano-silica additive was the least affected cement samples after acid treatment as compared to a similar weight of Class G OPC.

Drill bit is the most essential tool in drilling and drill bit selection plays a significant role in drilling process planning. This paper discusses bit selection by employing a method of combining Artificial Neural Network (ANN) and the computation of Genetic Algorithm (GA). In this method, offset well drilling records are used for training the ANN model and International Association Drilling Contractors (IADC) bit codes are used to name each bit. However, some researchers have used bit codes as input or output variables. This paper illustrates that the bit codes are better used in referring to the name of each bit instead of using them as values for calculation in the ANN model. The ANN black box was converted to white box to obtain a visible mathematical model for predicting the Rate of Penetration (ROP). This mathematical model, which was the objective function in the GAs, was used to find the optimum drilling values and to maximize the ROP. When drilling a new well, bit selection process requires the maximum ROP of a bit that corresponds to the optimum drilling parameters being obtained by combining the trained ANN model with GA. A bit selection example is provided by using the Shadegan oil field drilling data. The mean square error (MSE) obtained a value of 0.0037 whereas the coefficient of determination obtained a value of 0.9473. In other words, the predicted ROP model based on the field drilling data indicated a good correlation with the real ROP.

Global energy demand is rapidly increasing at a rate of 8% between 2004 and 2019, and conventional energy sources are unable to meet this requirement. Therefore, the oil and gas energy industry is working continuously to produce unconventional energy sources such as deepwater oil and gas reservoirs, gas hydrates, and geothermal energy using drilling operations. In the drilled borehole, the stability and integrity issues are encountered that are related to pressure differential generated by the Couette-Poiseuille (CP) fluid flow phenomenon occurred during pipe tripping operation. There is a lack of experimental data to comprehend the impact of CP flow on pressure differential using non-Newtonian fluid in concentric and eccentric annuli, especially in petroleum exploration. Hence, an experimental investigation is performed to replicate the tripping operations during oil and gas well drilling. The investigation provides an extensive understanding of the physical and mechanical characteristics of non-Newtonian fluids under CP flow at varying inner pipe tripping speeds (0.030481–0.21336 m/s), rheological properties, and geometrical dimensions. The results indicate that surge/swab pressure gradient tends to decrease with increasing eccentricity (e) and increase with increasing diameter ratio (δ) and pipe tripping speed (Vp). Furthermore, the pressure differential rate is considerably high at low tripping speed but reduces at high pipe tripping speed due to the shear-thinning behavior of YPL fluid. The study also reveals that gel strength increases the surge/swab pressure gradient by approximately 18%. A multi-variable correlation is developed to predict surge/swab pressure gradient, exhibiting an excellent agreement with experimental data and existing theoretical models.