Prediction of drilling fluid lost-circulation zone based on deep learning

Lost circulation has become a crucial technical problem that restricts the quality and efficiency improvement of the drilling operation in deep oil and gas wells. The lost-circulation zone prediction has always been a hot and difficult research topic on the prevention and control of lost circulation. This study applied machine learning and statistical methods to deeply mine 105 groups and 29 features of loss data from typical loss block M. After removing 10 sets of noise data, the methods of mean removal, range scaling and normalization were used to pre-treat the 95 sets of the loss data. The multi-factor analysis of variance (ANOVA) and random forest algorithm were adopted to determine the 13 main factors affecting the lost circulation. The three typical deep learning neural network models were improved, the parameters in the models were adjusted, the neural network models with different structures were compared according to the PR curves, and the best model structure was built. The pre-treated loss data in 95 sets with 13 features were divided into the training set and test set by a ratio of 4:1. The model performance was evaluated using F1 score, accuracy, and recall rate. The trained model was successfully applied to the G block with severe leakage. The results show that the capsule network model is better than the BP neural network model and the convolutional neural network model. It stabilizes at 300 training rounds, with a prediction accuracy of 94.73%. The improved model can be applied to lost-circulation control in the field and provide guidance on leakage prevention and plugging operations

Stress-dependent Mohr–Coulomb shear strength parameters for intact rock

Rock strength is imperative for the design and stability analysis of engineering structures. The Mohr–Coulomb (M-C) criterion holds significant prominence in geotechnical engineering. However, the M-C criterion fails to accurately capture the nonlinear strength response and neglects the critical state of rocks, potentially leading to inaccuracies in the design phase of deep engineering projects. This study introduces an innovative stress-dependent friction angle and cohesion (SFC) for the M-C criterion to capture the nonlinear strength responses of intact rocks, spanning from non-critical to critical states (brittle to ductile regions). A novel method for determining these stress-dependent parameters at each corresponding σ3 is initially introduced. Subsequently, an examination of the confinement dependency of the friction angle and cohesion is conducted, leading to the derivation of the SFC model. The SFC-enhanced M-C criterion, utilizing parameters obtained from triaxial tests under lower σ3, demonstrates the capability to delineate the complete non-linear strength envelope from brittle to ductile regions. Validation through triaxial test data confirms that the predictions of the SFC-enhanced M-C criterion accurately correspond to the strength characteristics of the tested rocks

A critical review of capillary pressure behavior and characterization in fractional-wet reservoirs

Fractional wettability is common in oil and gas reservoirs, resulting in complex fluid distribution and transport phenomena. A precise understanding of capillary pressure behavior and characterization in fractional-wet reservoirs, including the two-phase flow mechanisms within pores and relationship between capillary pressure and saturation in porous media, is significant to enhanced oil recovery strategies. In this paper, an in-depth review of the two-phase flow mechanisms in fractional-wet pores and capillary entry pressures in various displacement processes was conducted. Furthermore, the effects of oil-wet proportion and contact angle on capillary pressure characterization were summarized, highlighting the emergence of similar capillary pressure curves under conditions of low oil-wet proportions. The prediction models for capillary pressure, containing empirical equations and physics-based models were discussed, with the aim of clarifying the most effective prediction methodologies. Finally, the review was finalized by outlining key findings and future directions for both experimental and theoretical studies in the realm of capillary pressure behavior and characterization.Document Type: Invited reviewCited as: Xiao, Y., You, Z., Wang, L., Du, Z. A critical review of capillary pressure behavior and characterization in fractional-wet reservoirs. Capillarity, 2024, 10(1): 12-21. https://doi.org/10.46690/capi.2024.01.0

Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection

This study introduces an innovative data-driven and machine-learning framework designed to accurately predict site scores in the site screening study for specific offshore CO2 storage sites. The framework seamlessly integrates diverse sub-surface geospatial data sources with human aided expert-weighted criteria, thereby providing a high-resolution screening tool. Tailored to accommodate varying data accessibility and the significance of criteria, this approach considers both technical and non-technical factors. Its purpose is to facilitate the identification of priority locations for projects associated with Carbon Capture, Utilization, and Storage (CCUS). Through aggregating and analyzing geospatial datasets, the study employs machine learning algorithms and an expert-weighted model to identify suitable geologic CCUS regions. This process adheres to stringent safety, risk control, and environmental guidelines, addressing situations where human analysis may fail to recognize patterns and provide detailed insights in suitable site screening techniques. The primary emphasis of this research is to bridge the gap between scientific inquiry and practical application, facilitating informed decision-making in the implementation of CCUS projects. Rigorous assessments encompassing geological, oceanographic, and eco-sensitivity metrics contribute valuable insights for policymakers and industry leaders. To ensure the accuracy, efficiency, and scalability of the established offshore CO2 storage facilities, the proposed machine learning approach undergoes benchmarking. This comprehensive evaluation includes the utilization of machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Extreme Learning Machine (MLELM), and Deep Neural Network (DNN) for predicting more suitable site scores. Among these algorithms, the DNN algorithm emerges as the most effective in site score prediction. The strengths of the DNN algorithm encompass nonlinear modeling, feature learning, scale invariance, handling high-dimensional data, end-to-end learning, transfer learning, representation learning, and parallel processing. The evaluation results of the DNN algorithm demonstrate high accuracy in the testing subset, with values of AAPD (Average Absolute Percentage Difference) = 1.486 %, WAAPD (Weighted Average Absolute Percentage Difference) = 0.0149 %, VAF (Variance Accounted For) = 0.9937, RMSE (Root Mean Square Error) = 0.9279, RSR (Root Sum of Squares Residuals) = 0.0068, and R2 (Coefficient of Determination) = 0.9937

A critical review of capillary pressure behavior and characterization in fractional-wet reservoirs

Fractional wettability is common in oil and gas reservoirs, resulting in complex fluid distribution and transport phenomena. A precise understanding of capillary pressure behavior and characterization in fractional-wet reservoirs, including the two-phase flow mechanisms within pores and relationship between capillary pressure and saturation in porous media, is significant to enhanced oil recovery strategies. In this paper, an in-depth review of the two-phase flow mechanisms in fractional-wet pores and capillary entry pressures in various displacement processes was conducted. Furthermore, the effects of oil-wet proportion and contact angle on capillary pressure characterization were summarized, highlighting the emergence of similar capillary pressure curves under conditions of low oil-wet proportions. The prediction models for capillary pressure, containing empirical equations and physics-based models were discussed, with the aim of clarifying the most effective prediction methodologies. Finally, the review was finalized by outlining key findings and future directions for both experimental and theoretical studies in the realm of capillary pressure behavior and characterization

The hole sealing technology of solid-liquid materials with three pluggings and two injections for gas extraction hole in the coal mine

The sealing quality of the gas extraction holes determines the extracted gas concentration. Based on this, the paper reveals the basic principle of hole sealing by analyzing the gas leakage mechanism of the borehole. The hole sealing technology of solid-liquid materials with three pluggings and two injections for the gas extraction hole is proposed, and the hole sealing device and material are developed. Through testing the granularity distribution of the solid material, as well as the surface tension and contact angle of the slurry, the hole sealing material that can meet the requirements of accessible, sticky, and anti-deformation is selected. The sealing material enters microcracks and bonds coal rock more easily. First, the solid material is injected for hole sealing. Second, the liquid material can be injected repeatedly to maintain a high concentration for holes with poor sealing and gas concentration attenuation in the late stage of gas extraction. Field tests show that the gas concentration of solid material is 1.3 times that of the conventional material after 30 days of sealing. The liquid material injected after the concentration decline enables the gas extraction concentration to be recovered at 85 %

Effect of flow velocity on clogging induced by coal fines in saturated proppant packs: A transition from surface deposition to bridging

Hydraulic fracturing is a widely used technique to enhance the production of coalbed methane reservoirs. However, a common issue is the invasion of coal fines into proppant packs, leading to pore clogging and reduced conductivity. This study investigated the impact of flow velocity on clogging by coal fines in saturated proppant packs to optimize the flow velocity and alleviate clogging during dewatering. Clogging experiments induced by coal fines were conducted on saturated proppant packs with varying superficial velocities. Throughout each experiment, the permeability and effluent concentration were monitored, and the process of clogging was visually observed using an optical microscope. The experimental results showed that both permeability and effluent concentration initially increased and then decreased with an increase in flow velocity, indicating the existence of a critical flow velocity for minimizing clogging in proppant packs. Microscale observations revealed that the dominant regimes of clogging induced by coal fines at low and high flow velocities were surface deposition and hydrodynamic bridging, respectively; a critical flow velocity was required to induce the occurrence of bridging. Removal efficiencies of coal fines in relation to surface deposition and straining against flow velocity were theoretically analyzed, aiming to provide insights into the mechanisms underlying the impact of flow velocity on clogging. The results showed that the overall removal efficiency by surface deposition and straining decreased with an increase in flow velocity. Theoretical data matched well with the experimental results at low flow velocities but failed to explain the outcomes at high flow velocities, primarily due to the onset of bridging at high flow velocities. This study highlights the necessity of developing a removal efficiency model for bridging to accurately describe clogging by coal fines in proppant packs and provides recommendations for clogging control in proppant packs

Shear thickening effects of drag-reducing nanofluids for low permeability reservoir

Drag-reducing nanofluids are complex non-Newtonian fluids. Their constitutive characteristics are the basis of flow mechanism analysis in porous media. However, the rheological effects of drag-reducing nanofluids have not been thoroughly studied. In the present work, rheological properties of several nanofluids were measured, and the shear thickening mechanism was investigated experimentally. The results show that all the nanofluids examined have complex characteristics and critical shear rates. The viscosity exhibits a slow linear increase with the shear rate below the critical shear rate, while the shear thickening power-law fluid behaviour appears above the critical shear rate. The critical shear rate increases with the increase of particle concentration, which indicates the injection rate needs to be controlled to avoid significant increase of nanofluids viscosity. The rheological curve of increasing shear rate nearly coincides with that of decreasing shear rate, which indicates that the shear thickening of nanofluids studied in this work is transient and reversible. A constant index constitutive equation with an exponent of 0.5 is obtained from test results by the fixed index method, and its coefficient k(c) is a linear function of the concentration, which can replace a set of conventional constitutive equations with different concentrations. The constant index constitutive equation also clarifies the coefficient dimension. Similar results have been obtained by analysing several other nanofluids using the fixed index method, which validates the new effective method for constructing the constitutive equations of non-Newtonian nanofluids.Cited as: Gu, C., Qiu, R., Liu, S., You, Z., Qin, R. Shear thickening effects of drag-reducing nanofluids for low permeability reservoir. Advances in Geo-Energy Research, 2020, 4(3): 317-325, doi: 10.46690/ager.2020.03.0

A new volumetric strain-based method for determining the crack initiation threshold of rocks under compression

The crack initiation stress threshold ( ci) is an essential parameter in the brittle failure process of rocks. In this paper, a volumetric strain response method (VSRM) is proposed to determine the σci based on two new concepts, i.e., the dilatancy resistance state index ( ci) and the maximum value of the dilatancy resistance state index difference (| ci|), which represent the state of dilatancy resistance of the rock and the shear sliding resistance capacity of the crack-like pores during the compressive period, respectively. The deviatoric stress corresponding to the maximum | ci| is taken as the ci . We then examine the feasibility and validity of the VSRM using the experimental results. The results from the VSRM are also compared with those calculated by other strain-based methods, including the volumetric strain method (VSM), crack volumetric strain method (CVSM), lateral strain method (LSM) and lateral strain response method (LSRM). Compared with the other methods, the VSRM is effective and reduces subjectivity when determining the ci . Finally, with the help of the proposed VSRM, influences from chemical corrosion and confining stress on the ci and ci of the carbonate rock are analyzed. This study provides a subjective and practical method for determining σci . Moreover, it sheds light on the effects of confinement and chemical corrosion on σci

An innovative fracture plugging evaluation method for drill-in fluid loss control and formation damage prevention in deep fractured tight reservoirs

Lost circulation, resulting from the undesired loss of drilling fluid into formation fractures, stands as a significant technical obstacle in the exploration and production of oil, gas, and geothermal reservoirs. Effective mitigation of this challenge requires the development and application of robust experimental evaluation methods to assess the effectiveness of fracture plugging. The traditional approach to fracture plugging evaluation relies on a uniform evaluation index and experimental parameters for various lost circulation types. Unfortunately, this practice frequently results in inconsistent performance of loss control formulas during laboratory experiments and field tests. To address this issue, this paper introduces an innovative evaluation method that accounts for the specific characteristics of the three major lost circulation types. By adopting this approach, a more scientifically rigorous design and optimization of loss control formulas can be achieved, ensuring their effectiveness in managing lost circulation challenges. The development of the new method involves a systematic five-step establishment process: lost circulation type determination, evaluation index weight calculation, fitness degree analysis between laboratory experiment and field test, experimental parameters optimization, and quantitative scoring of loss control formula. Analytic hierarchy process is adopted to calculate the evaluation index weight. Quantitative scoring model is proposed to finally determine the integrated formula score for the quantitative evaluation and scientific optimization of loss control formula. To bridge the gap between laboratory and field applications, laboratory evaluation tests are developed to address different types of lost circulation scenarios. The experimental results demonstrate significant improvements achieved through the optimized formula. Specifically, the maximum plugging pressure increased from 5 MPa to 20.8 MPa, while the initial and cumulative loss volumes witnessed reductions of 30.3 ml and 121.2 ml, respectively. Moreover, the evaluation method proposed in this paper exhibits a fitting degree of over 90 % when compared to the actual control effect on drilling fluid loss. These findings substantiate the successful establishment of a connection between laboratory evaluations and field performance, providing valuable insights for future applications. Finally, a novel evaluation method for assessing the fracture plugging effect is established, accounting for various lost circulation types in deep fractured tight reservoirs. The reliability of this proposed evaluation method is validated by field test. Building upon this method, a high-score formula is designed and effectively deployed in a deep fractured tight reservoir in Tarim Basin, China. The successful application highlights the practical value and robustness of the developed evaluation method, offering promising prospects for future operations in similar reservoir settings