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This article presents extensive analysis and review on recent developments in smart fluids as well as future opportunities of smart drilling fluids utilization in oil and gas well drilling while focusing on the following smart fluids: smart nanoparticles, electrorheological, magnetorheological, and viscoelastic surfactant (VES) fluids. The distinctive properties of nanoparticles such as tiny particle sizes, high specific surface area, mechanical strength, and thermal stability make them suitable for utilization in drilling fluids. In bentonite water-based drilling fluid systems, this review suggests that charged nanoparticles are capable of displacing exchangeable ions in between bentonite clay platelets, thereby forming intercalates which can interact with clay surfaces through electrostatic attraction or repulsion. In improving wellbore stability, it is presented in this review that nanoparticles are able to invade and plug ultratiny pore spaces in shale formations, thereby further enhancing shale formations’ mechanical strength and wellbore stability. According to this review, the magnitude of changes in properties of smart electrorheological and magnetorheological fluids largely depends on the intensity of applied electric and magnetic fields. The intensity of smart fluids properties alteration due to applied field would equally depend on wt.% concentration and chemical compositions of particles susceptible to electric and magnetic fields. Based on review carried out on VES smart fluids, attractive and repulsive forces in the smart VES fluids solution result in the formation of micelles which can cause changes in viscoelastic property of the formulated smart viscoelastic fluids. The more the concentration of charged ions in the base fluid which VES fluids come in contact with, the higher the viscoelasticity of the smart VES fluids. According to this review, utilization of smart materials in drilling fluids can result in meeting oil and gas well drilling technical challenges including enhancing wellbore stability, improving hole cleaning performance, lost circulation control, fluid loss control, enhancing rate of penetration, pressure drop control, and easing cutting carrying efficiency of drilling fluids. This review equally suggests that the utilization of smart fluids such as smart magnetorheological and electrorheological fluids would facilitate drilling automation and real-time data acquisition processes, which is the future technology in oil and gas drilling.

This work addresses the ongoing challenge of optimizing sand control screens, attributed to a limited understanding of screen blocking. A novel one‐way computational fluid dynamics and discrete element method (CFD–DEM) coupling technique is presented to analyze the structural parameters of unconsolidated prepacked gravel screens (PPGS). CFD–DEM involves systematically investigating the width of the punched slot on the outer protective screen, the size of the gravel, and the efficiency of gravel packing to improve the antiblocking capability of unconsolidated PPGS. Results from numerical simulations reveal that under conditions of effective sand control, the amount of sand entering the gravel layer to the bridge (referred to as the sand pass rate) is positively correlated with the antilocking ability (referred to as oil productivity index) for unconsolidated PPGS. The efficiency of gravel packing has the most substantial effect on the antilocking performance of unconsolidated PPGS. The data suggest that appropriately reducing the efficiency of gravel packing can enhance the antilocking capability of unconsolidated PPGS. To design an unconsolidated PPGS with enhanced antilocking capability, the efficiency of gravel packing be determined first. Then, the gravel size is designed based on this efficiency, and the width relative to the gravel size is decided. This contemporary design principle diverges considerably from previous design principles for gravel‐packed sand control screens. Supporting validation experiments agree with simulation outcomes, suggesting the established one‐way CFD–DEM coupling method in this paper is suitable for analyzing the plugging mechanism of unconsolidated PPGS. Thus, the CFD–DEM coupling method improves the design of such screens for improved antiblocking performance. [ABSTRACT FROM AUTHOR]

The paper concerns the influence of time and strain-rate effects on the clays in one-dimensional consolidation under constant effective stress. An improved creep constitutive model is deduced, by analyzing the stress-strain theory developed by yin and sekiguchi. Treating the sample as a single system and applying the boundary conditions at the system level, differential mathematical equations to the consolidation problem of clays are obtained. The proposed differential mathematical equations have advantages in their ability to (i) not clarify the primary consolidation and secondary consolidation deformation. The error in calculating consolidation deformation which is caused by the argument about end of primary consolidation can be avoided. (ii) obtain the model parameters easily. How to achieve parameters by experiment is described in detail in the paper. (iii) be programmed and solved readily for the finite difference description of the problem. Results from clays have been used to examine the validity of the model. It is shown that the proposed model can describe the consolidation of clays well.

A wellbore's cleanliness affects drilling efficiency, economy and drilling safety directly in extended-reach drilling operations. Wellbore cleaning in extended-reach drilling has always been a tough problem. Field experience has shown that inefficient transport of small cuttings is a main factor for excessive drag and torque during extended-reach drilling. However, very little is known about the transport of small cuttings. In this paper, we use the data fitting method of dimensional analysis and regression analysis to establish a wellbore cleaning model for high-inclination sections, then use water and Polyanionic Cellulose (PAC) as a drilling fluid to analyze the settlement cleaning of small cuttings in the wellbore. The data from the example wells were used for field simulations to finally derive many factors like drilling fluid density, drilling fluid displacement, drill pipe rotation speed, diameter of cuttings and annulus hydraulic diameter, which affect the thickness of the dimensionless cutting bed. The results show that the dimensionless cutting thickness decreases with increasing drilling fluid density, drilling fluid displacement and drill pipe rotation speed; and increases with increasing cutting size and annular hydraulics diameter. Meanwhile, the effect of drilling fluid discharge, drill pipe rotation speed and cutting size on the thickness of the dimensionless cutting bed decreases as their values increase. This model can be utilized to design parameters in drilling and to predict the transport of cuttings in high-inclination sections. The successful establishment of the wellbore cleaning model for small cuttings in high-inclination sections of extended-reach drilling wells is highly innovative, which is a successful combination of theory and practice and has important guiding significance for promoting the development of wellbore cleaning technology. [ABSTRACT FROM AUTHOR]

Formation leak-off pressure, which sets the upper limit of the safe drilling fluid density window, is crucial for preventing wellbore accidents and ensuring safe and efficient drilling operations. The paper thoroughly examines models of drilling physics alongside artificial intelligence techniques. The study introduces a dual-driven method for predicting reservoir pore pressure by integrating long short-term memory (LSTM) and backpropagation (BP) neural networks, where the core component is the LSTM-BP neural network model. The input data for the LSTM-BP model include wellbore diameter, formation density, sonic time, natural gamma, mud content, and pore pressure. The study demonstrates the practical application of the method using two vertical wells in Block M, employing the M-1 well for training and the M-2 well for validation. Two distinct input layer configurations are devised for the LSTM-BP model to evaluate the influence of formation density on prediction accuracy. Notably, Scheme 2 omits formation density as a variable in contrast to Scheme 1. The study's results indicate that, for input layer configurations corresponding to Scenario 1 and Scenario 2, the LSTM-BP model exhibits relative error ranges of (−2.467%, 2.510%) and (−6.141%, 5.201%) on the test set, respectively. In Scenario 1, the model achieves mean squared error (MSE), mean absolute error (MAE), and R-squared (R2) values of 0.000229935, 0.011198329, and 0.92178272, respectively, on the test set. Conversely, for Scenario 2, the model demonstrates a substantial escalation of 992.393% and 240.674% in MSE and MAE, respectively, compared to Scenario 1; however, R2 diminishes by 66.920%. Utilizing the trained LSTM-BP model, predictions for formation lost pressure in Well M-2 reveal linear correlation coefficients of 0.8173 and 0.6451 corresponding to Scenario 1 and Scenario 2, respectively. These findings imply that the predictions from the Scenario 1 model demonstrate stronger alignment with results derived from formulaic calculations. These observations remain consistent for both the BP neural network algorithm and the random forest algorithm. The aforementioned research results not only highlight the elevated predictive precision of the LSTM-BP model for intelligent prediction of formation lost pressure, a product of this study, thereby furnishing valuable data points to enhance the security of drilling operations in Block M, but also underscore the necessity of deliberating both physical relevance and data correlation during the selection of input layer variables. [ABSTRACT FROM AUTHOR]

In recent years, there has been significant research and practical application of machine learning methods for predicting reservoir pore pressure. However, these studies frequently concentrate solely on reservoir blocks exhibiting normal-pressure conditions. Currently, there exists a scarcity of research addressing the prediction of pore pressure within reservoir blocks characterized by abnormally high pressures. In light of this, the present paper introduces a machine learning-based approach to predict pore pressure within reservoir blocks exhibiting abnormally high pressures. The methodology is demonstrated using the X block as a case study. Initially, the combination of the density–sonic velocity crossplot and the Bowers method is favored for elucidating the overpressure-to-compact mechanism within the X block. The elevated pressure within the lower reservoir is primarily attributed to the pressure generated during hydrocarbon formation. The Bowers method has been chosen to forecast the pore pressure in well X-1. Upon comparison with real pore pressure data, the prediction error is found to be under 5%, thus establishing it as a representative measure of the reservoir's pore pressure. Intelligent prediction models for pore pressure were developed using the KNN, Extra Trees, Random Forest, and LightGBM algorithms. The models utilized five categories of well logging data, sonic time difference (DT), gamma ray (GR), density (ZDEN), neutron porosity (CNCF), and well diameter (CAL), as input. After training and comparison, the results demonstrate that the LightGBM model exhibits significantly superior performance compared to the other models. Specifically, it achieves R2 values of 0.935 and 0.647 on the training and test sets, respectively. The LightGBM model is employed to predict the pore pressure of two wells neighboring well X-1. Subsequently, the predicted data are juxtaposed with the actual pore pressure measurements to conduct error analysis. The achieved prediction accuracy exceeds 90%. This study delivers a comprehensive analysis of pore pressure prediction within sections exhibiting anomalously high pressure, consequently furnishing scientific insights to facilitate both secure and efficient drilling operations within the X block. [ABSTRACT FROM AUTHOR]

Underground coal gasification (UCG) is a promising technology for extracting synthesis gas from coal seams through in situ gasification. This study aims to investigate the thermo-mechanical behavior and integrity of the surrounding rock in the gasification vicinity to facilitate safe and efficient UCG operations. Rock property testing experiments are conducted under varying temperature conditions, revealing significant temperature dependencies for the thermal and mechanical parameters. A thermo-mechanical coupling model is developed to analyze the stress and damage distribution near the gasification chamber. The influence of the temperature dependency of stress states and failure risks during the gasification process is evaluated. In addition, the effects of panel orientation, chamber width, maintaining duration, operating temperature and operating pressure on the failure behavior of the gasification surrounding rocks are illustrated through parametric analysis. The findings have practical implications for the design and assessment of UCG processes, enhancing the safety and efficiency of coal gasification operations. [ABSTRACT FROM AUTHOR]