Real Reservoir Research Articles

Investigation on the Impact of CO2-Induced Precipitation on Microscopic Pore Structure of Low-Permeable Reservoirs

CO2 injection into reservoirs might induce organic or inorganic precipitation in microscopic pores, and some of the pores are blocked by the precipitates, resulting in a decrease in the connectivity of pores and affecting oil recovery. In this paper, a set of experiments on CO2 displacement and CO2-water-oil-rock interaction are conducted to study the microscopic mechanisms of reservoir damage. In-situ Nuclear Magnetic Resonance (NMR) technology is applied to conduct the microscopic analysis of CO2-EOR and reservoir damage at real reservoir conditions. The results reveal that the particle size and the amount of inorganic precipitates formed in the formation water will increase with the rise of CO2 injection pressure, and the inorganic precipitates primarily consist of siderite and kaolinite. The change of NMR T2 spectra of the core samples reveals that the inorganic precipitates primarily block the small pores less than 0.89 μm. However, as a result of stronger mineral dissolution and solute transport, the pore volume of the large pores (greater than 0.89 μm) increases after the CO2-water-rock reaction. Ultimately, this interaction results in a 5.4% increment in pore volume. In addition, the displacement efficiency of water-saturated cores is improved after the first CO2 displacement, which indicates that CO2-water-rock interaction can effectively improve the seepage properties of reservoirs. Nevertheless, the reservoir damage caused by asphaltene precipitation during CO2 flooding is more significant. The asphaltene precipitation percentage in formation oil increases with the rise of CO2 injection pressure, reservoir temperature, and asphaltene content in virgin oil. CO2-induced asphaltene precipitation causes more severe blockage on small pores in comparison with large pores. After two hours of CO2 injection to the oil-saturated core, permeability and oil recovery caused by CO2-induced precipitation decreased by 17.6% and 11.4%, respectively.

An empirical model for predicting saturation pressure of pure hydrocarbons in nanopores

Due to the confinement and strong adsorption to the pore wall in meso‑ and nano pores, fluid phase behavior in the confined media, such as the tight and shale reservoirs, can be significantly different from that in the bulk phase. A large amount of work has been done on the theoretical modeling of the phase behavior of hydrocarbons in the confined media. However, there are still inconsistencies in the theoretical models developed and validations of those models against experimental data are inadequate.In this study, we conducted a comprehensive review of experimental work on the phase behavior of hydrocarbons under confinement and analyzed various theoretical phase-behavior models. Emphasis was given to the modifications to the Peng-Robinson equation of state (PR EoS). Through the comparative analysis, we developed a modified alpha-function in PR EoS for accurate prediction of the saturation pressures of hydrocarbons in porous media. This modified alpha-function accounts for the pore size and was derived based on the regression results through minimizing the deviation between the experimentally measured and numerically calculated saturation pressure data. Meanwhile, the thermodynamic properties of propane were calculated in the bulk phase and in the nanopores. Finally, we validated the newly developed model using the experimental data in synthesized mesoporous materials and real reservoir rocks.By applying the modified PR EoS, a more accurate representation of the experimentally measured saturation pressure data in confined nanopores was achieved. This newly developed model not only enhanced the accuracy of the predictions but also provided valuable insights into the confinement effects on the phase behavior of hydrocarbons in nanopores. Notably, we observed significant changes in the properties of propane within confined nanopores, including suppressed saturation pressure and fugacity, indicating a greater tendency for the gas to remain in the liquid phase. Enthalpy of vaporization was found to increase highlighting increased difficulty in transitioning from liquid to gas phase under confinement. Additionally, the new model predicts an increased gas compressibility factor in the nanopores suggesting a close resemblance of ideal gas due to the counterbalance between the attractive and repulsive forces. To validate the model, new datasets containing saturation pressures of propane and ethane under a wide range of temperatures and pore sizes were employed. The newly developed model was further applied to the experimental data obtained in real rock samples (sandstones, limestones, and shales). Interestingly, it was observed that the phase change in these samples predominantly occurred in the smallest pores. This finding highlights the importance of considering the pore size distribution when studying the phase behavior of hydrocarbons in a capillary medium even if the rock has high permeability.This study provided a simple and easy-to-implement modification to the PR EoS for accurate prediction of the phase behavior of petroleum fluids under confinement. The newly developed model for calculating saturation pressures of hydrocarbons demonstrates improved accuracy in representing experimental data compared to the previous models, while offering a more straightforward and simplified approach.

Reflection, transmission, and AVO response of inhomogeneous plane waves in thermoporoelastic media with two-temperature equations of heat conduction

We develop a modified fluid-saturated thermoporoelastic model by introducing two temperature equations to account for the temperature differences between the solid skeleton and the pore filling. The modified two-temperature generalized thermoporoelastic (TTG) equation is an extension of the classical single-temperature Lord-Shulman (LS), Green-Lindsay, and generalized LS theories. It predicts four compressional waves and one shear wave based on the analysis of inhomogeneous plane waves. We study the exact reflection and transmission (R/T) coefficients at the interface separating two thermoporoelastic half-spaces and develop an amplitude-variation-with-offset (AVO) approximation. Comparison with the Biot poroelastic case of the water/oil contact shows that the TTG model reproduces the exact R/T results. The AVO response of oil, gas, and real CO2 geosequestration reservoirs illustrates the practical applicability of our model and provides the theoretical basis for the exploration of high-temperature resources.

Production optimization in a fractured carbonate reservoir with high producing GOR

The Gas-Oil Ratio (GOR) is a crucial production parameter in oil reservoirs. An increase in GOR results in higher gas production and lower oil production, potentially leading to well shut-ins due to economic infeasibility. This study focuses on a real fractured oil field that requires urgent production operations to reduce the producing GOR. In this study, the static model for the field was developed using commercial software, involving steps such as data collection, fault modeling, meshing, and statistical analysis to prepare for dynamic simulation. The dynamic model incorporates geometry, gridding, and rock properties from the static model, utilizing a dual-porosity approach for the naturally fractured reservoir and the Peng-Robinson equation for fluid phase behavior. Initial reservoir conditions, production history, and rock-fluid interactions were defined, with relative permeability curves indicating a water-wet reservoir and low critical gas saturation affecting the GOR. To better understand the relationship between reservoir and production parameters, a detailed sensitivity analysis was performed using the Response Surface Methodology (RSM). Following the sensitivity analysis, a history matching process was conducted using the Designed Exploration and Controlled Evolution (DECE) optimizer to validate the model for future forecasts. Six operational scenarios were defined to decrease the production GOR and enhance final recovery from the field. The results indicate that the water injection scenario is effective in preventing the GOR increase by maintaining reservoir pressure, thereby sustaining production over a longer period. This scenario also improves oil recovery by approximately 6% compared to the base case. Finally, optimization was carried out using the DECE optimizer for each scenario to fine-tune the operational parameters. The goal was to maximize oil revenue for each scenario during the optimization process. This study stands out as one of the few that provides a comprehensive analysis of production behavior and development planning for a real fractured reservoir with high producing GOR.

Geomechanical risk assessment for CO2 storage in deep saline aquifers

We study CO2 injection into a saline aquifer intersected by a tectonic fault using a coupled modeling approach to evaluate potential geomechanical risks. The simulation approach integrates the reservoir and mechanical simulators through a data transfer algorithm. MUFITS simulates non-isothermal multiphase flow in the reservoir, while FLAC3D calculates its mechanical equilibrium state. We accurately describe the tectonic fault, which consists of damage and core zones, and derive novel analytical closure relations governing the permeability alteration in the fault zone. We estimate the permeability of the activated fracture network in the damage zone and calculate the permeability of the main crack in the fault core, which opens on asperities due to slip. The coupled model is applied to simulate CO2 injection into synthetic and realistic reservoirs. In the synthetic reservoir model, we examine the impact of formation depth and initial tectonic stresses on geomechanical risks. Pronounced tectonic stresses lead to inelastic deformations in the fault zone. Regardless of the magnitude of tectonic stress, slip along the fault plane occurs, and the main crack in the fault core opens on asperities, causing CO2 leakage out of the storage aquifer. In the realistic reservoir model, we demonstrate that sufficiently high bottomhole pressure induces plastic deformations in the near-wellbore zone, interpreted as rock fracturing, without slippage along the fault plane. We perform a sensitivity analysis of the coupled model, varying the mechanical and flow properties of the storage layers and fault zone to assess fault stability and associated geomechanical risks.

Constraint optimization of an integrated production model utilizing history matching and production forecast uncertainty through the ensemble Kalman filter

The calibration of reservoir models using production data can enhance the reliability of predictions. However, history matching often leads to only a few matched models, and the original geological interpretation is not always preserved. Therefore, there is a need for stochastic methodologies for history matching. The Ensemble Kalman Filter (EnKF) is a well-known Monte Carlo method that updates reservoir models in real time. When new production data becomes available, the ensemble of models is updated accordingly. The initial ensemble is created using the prior model, and the posterior probability function is sampled through a series of updates. In this study, EnKF was employed to evaluate the uncertainty of production forecasts for a specific development plan and to match historical data to a real field reservoir model. This study represents the first attempt to combine EnKF with an integrated model that includes a genuine oil reservoir, actual production wells, a surface choke, a surface pipeline, a separator, and a PID pressure controller. The research optimized a real integrated production system, considering the constraint that there should be no slug flow at the inlet of the separator. The objective function was to maximize the net present value (NPV). Geological data was used to model uncertainty using Sequential Gaussian Simulation. Porosity scenarios were generated, and conditioning the porosity to well data yielded improved results. Ensembles were employed to balance accuracy and efficiency, demonstrating a reduction in porosity uncertainty due to production data. This study revealed that utilizing a PID pressure controller for the production separator can enhance oil production by 59% over 20 years, resulting in the generation of 2.97 million barrels of surplus oil in the field and significant economic gains.

An improved neural operator framework for large-scale CO2 storage operations

Geologic carbon storage (GCS) is a practical solution to mitigate the impact of climate change and achieve net-zero carbon emissions. However, due to complex subsurface characteristics and operational constraints, GCS can potentially induce seismic events and leakage to groundwater resources. Moreover, predicting subsurface response in pressure and saturation changes during GCS requires high-fidelity models with high computational times. Machine learning (ML) offers a promising solution to alleviate these challenges. Yet, ML-driven approaches in subsurface physics face significant obstacles, such as the substantial computational resources required for training large-scale models and the accuracy requirement as a reliable surrogate model. To tackle these issues, we propose an improved neural operator (INO) that has improved a DeepONet architecture to handle a realistic reservoir model efficiently, with computational efficiency and high accuracy.Our proposed INO framework has three key benefits: precision, adaptability, and cost-efficiency. For precision, it achieves an average root mean square error (RMSE) of 0.05% (1.5 psi) compared to the average reservoir pressure (3,150 psi) and a RMSE value of 0.016 in void fraction for CO2 saturation among testing cases. Although it is sensitive to high-error areas with steep state variable gradients, proper domain decomposition, ensemble methods, and/or integrating domain-specific knowledge could improve this limitation. For adaptability, the INO framework can be trained with a subset of the computational domain during each backpropagation to achieve better training adaptability, even for problems with high-dimensional inputs (i.e., heterogeneous fields). For example, it can be trained with random subsampling of only 0.2% of the full domain but can predict pressure and saturation in any space and time within the entire domain. For cost efficiency, training procedures with a subset of the whole computational domain significantly improve computational time. For example, for 90 training cases with 1.73 million cells and 50-time steps, the overall training time was about 2.5 h using a single GPU. A trained INO model takes 1 s per evaluation over 1.73 million and 50 time stamps, much faster than a high-fidelity simulator. Overall, the proposed INO approach with these improved benefits will enable us to perform large-scale GCS applications, enhancing the potential of ML models in the subsurface and other multiphysics problems.

Estimation of shale adsorption gas content based on machine learning algorithms

Shale gas is a clean and low-carbon natural gas resource. It mainly exists in both adsorbed and free states in pores and fractures. To accurately estimate the in-situ adsorption gas content (AGC), which is helpful in resource evaluation and development planning, methane isothermal adsorption data and geological parameters have been collected, such as total organic carbon (TOC) content, thermal maturity (Ro), siliceous mineral content (VQF), total clay content (VTC), water content (VWC), and temperature (T). Using machine learning (ML) methods, the in-situ AGC estimation models were constructed and optimized. Various geological factors affecting methane adsorption were evaluated, and an application was conducted in the Wufeng-Formation shale. The results reveal that the four ML models have higher accuracy in predicting Langmuir volume (VL) and Langmuir pressure (PL) than empirical formulas and linear regression models. Among the four ML models, the Random Forest Regression (RFR) and eXtreme Gradient Boosting Regression (XGBR) models perform the best, with R2 higher than 0.85. TOC and T are the main factors affecting methane adsorption, followed by Ro and VQF, while the importance of VTC and VWC is relatively low. According to different combinations of geological parameters, there are three schemes for ML model construction. Among them, scheme 1 based on all six geological parameters has the highest accuracy and is most beneficial to predicting AGC. Gradually reducing VWC, VTC, and VQF results in a slight decrease in accuracy, with R2 decreasing by at most about 6%, scheme 2 is suitable for rougher estimation of AGC. Further removal of T and Ro results in a significant decrease in accuracy, with R2 decreasing by up to 50% and MRE exceeding 30%, rendering scheme 3 unavailable for AGC prediction. The AGC of Wufeng-Longmaxi shale is successfully predicted based on XGBR model, with AGC mainly in 1.0 m3/ton-4.0 m3/ton. Overall, the ML models based on multiple geological parameters can simulate the real reservoir environment and achieve rapid and accurate estimation of in-situ AGC.

A Study on the Heterogeneity and Anisotropy of the Porous Grout Body Created in the Stabilization of a Methane Hydrate Reservoir through Grouting

To solve the sand problem during the depressurization of methane hydrate (MH), we proposed a method to build a porous grout body with sufficient permeability and strength around the wellbore through inhibitor pre-injection and grouting, and verified its effectiveness and potential in our previous research using artificial cores created with silica sand and alternative hydrates such as TBAB- hydrate and iso-butane hydrate. However, all of the artificial cores mentioned above were created with high homogeneity, injected, cured, and had their physical properties measured in the vertical direction, which differs from real reservoir conditions. To investigate the effects of grouting in a more realistic fluid flow, we conducted further experiments using horizontal 1D cores, 1D cubic models, and a 2D cross-sectional model mimicking the near wellbore. These experiments revealed that (1) the generated gas somewhat suppressed the effects of grouting as in the case of previous experiments, and (2) grouted reservoirs would be heterogenous and anisotropic due to the fluid densities and the distribution of grout particles and turbidite sediments, but sufficient permeability and satisfactory strength could still be attained. The above series of experiments demonstrated that our method has the potential to effectively produce actual MH, preventing sand problems even in heterogeneous and anisotropic grouted reservoirs.

Automated Reservoir Characterization of Carbonate Rocks using Deep Learning Image Segmentation Approach

Summary The objective of this study is to develop a systematic and novel workflow for the automated and objective characterization of carbonate reservoirs with the help of deep learning architectures. An image database of more than 6,000 carbonate thin-section images was generated using the optical microscope and image augmentation techniques. Five features, namely clay/silt/mineral, calcite, pores, fossils, and opaque minerals, were identified with the help of manual petrography of the thin sections under the microscope. A total of four deep learning models were developed, which included U-Net, U-Net with ResNet34 backbone, U-Net with Mobilenetv2 backbone, and LinkNet with ResNet34 backbone. The Ensemble model of U-Net + ResNet34 and U-Net + MobileNetv2 yielded the highest intersection over union (IoU) score of 75%, followed by the U-Net + ResNet34 model with an IoU score of 61%. The models struggled with class imbalance, which was very prominent in the image database, with classes such as fossils and opaques considered to be rare. The statistical analysis of the relative errors revealed that the major classes play a more important role in increasing the final IoU score as opposed to the common understanding that the rare classes affect the model performance. The novel workflow developed in this paper can be extended to real carbonate reservoirs for time efficient, objective, and accurate characterization.

Reservoir Simulations of Hydrogen Generation from Natural Gas with CO2 EOR: A Case Study

This paper addresses the problem of hydrogen generation from hydrocarbon gases using Steam Methane Reforming (SMR) with byproduct CO2 injected into and stored in a partially depleted oil reservoir. It focuses on the reservoir aspects of the problem using numerical simulation of the processes. To this aim, a numerical model of a real oil reservoir was constructed and calibrated based on its 30-year production history. An algorithm was developed to quantify the CO2 amount from the SMR process as well as from the produced fluids, and optionally, from external sources. Multiple simulation forecasts were performed for oil and gas production from the reservoir, hydrogen generation, and concomitant injection of the byproduct CO2 back to the same reservoir. EOR from miscible oil displacement was found to occur in the reservoir. Various scenarios of the forecasts confirmed the effectiveness of the adopted strategy for the same source of hydrocarbons and CO2 sink. Detailed simulation results are discussed, and both the advantages and drawbacks of the proposed approach for blue hydrogen generation are concluded. In particular, the question of reservoir fluid balance was emphasized, and its consequences were presented. The presented technology, using CO2 from hydrogen production and other sources to increase oil production, also has a significant impact on the protection of the natural environment via the elimination of CO2 emission to the atmosphere with concomitant production of H2.

Mechanistic analysis of acid gas storage and oil recovery in naturally fractured reservoirs using single matrix block approach

AbstractThe objective of this study is to assess the storage of acid gas, containing CO2 and H2S, in a depleted naturally fractured reservoir (NFR) using single matrix block (SMB) approach. The acid gas dissolution in oil is considered by Peng‐Robinson equation of state and compositional simulation. The PHREEQC package is used to determine acid gas solubility in formation brine. Three types of acid gases with different compositions are used for this study and their swelling behavior and miscibility in relation to the reservoir oil are analyzed. An SMB model, with a matrix block surrounded by fractures, is constructed, and validated for simulation of a real experiment. The simulation is conducted for synthetic and real reservoir fluids when the oil is in its residual saturation. A sensitivity analysis is performed to study the effects of key parameters, such as acid gas composition, reservoir pressure, permeability, porosity and matrix height on the storage capacity and oil recovery factor. The matrix has a volume of 27 m3 and about half of acid gas storage is achieved in the first 5 years while the simulations are run for 30 years. The results show that up to 90% of remained oil is recoverable, and more than 0.67 kmol of acid gas per cubic meter of matrix is stored whether matrix contains a real oil or a synthetic one. Higher storage is achieved for higher matrix porosities and heights and large H2S proportion in acid gas. In all cases about 10% of acid gas is trapped in water and the remaining 90% is dissolved in oil. The mineral trapping was more active in CO2‐rich acid gases. While about 10 kg of the matrix rock was dissolved in the acidic brine when the acid gas contained H2S, the amount of the dissolved minerals in acidic brine resulted from the injection of CO2‐rich acid gas was more than 16 kg. Finally, this study gives a comparative analysis of the storage performance of acid gas mixture and pure CO2. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Influence of breach parameter models on hazard classification of off-stream reservoirs

The classification of dams or off-stream reservoirs concerning potential hazards in the event of failure often involves the use of two-dimensional hydraulic models for computing floodwave effects. These models necessitate defining breach geometry and formation time, for which various parametric models have been proposed. These models yield different values for average breach width, time of failure, and consequently, peak flows, as demonstrated by several researchers. This study analyzed the effect of selecting a breach parametric model on the hydraulic variables, potential damages, and hazard classification of structures. Three common parametric models were compared using a set of synthetic cases and a real off-stream reservoir. Results indicated significant effects of model choice. Material erodibility exerted a significant impact, surpassing that of failure mode. Other factors, such as the Manning coefficient, significantly affected the results. Utilizing an inadequate model or lacking information on dike material can lead to overly conservative or underestimated outcomes, thereby affecting hazard classification.

MODEL-BASED ANALYSIS TO EVALUATE THE EFFECT OF POLYMER PROPERTIES AND PHYSICAL PHENOMENA ON POLYMER FLOODING OPERATIONS

Polymer flooding, known for enhancing heavy oil displacement, may encounter efficiency-limiting physical phenomena. This work assesses the impact of key factors like viscosity, shear rate, and adsorption in field applications using a model-based approach on the EPIC001 case, a real offshore Brazilian heavy oil reservoir. In simulation results, increasing the displacing fluid viscosity using apparent or zero-shear functions, oil production and economic returns show improvements over waterflooding. The shear rate effect slightly increases oil production and enhances injectivity loss due to shear-thinning in polymer flow. However, modeling it increases computation costs, as it extends simulation run time from minutes to days, making it impractical for intensive processes like production optimization. An analysis of the method’s effectiveness shows that it varies based on the adsorption level considered. At its highest value, even with a higher oil recovery factor, the economic return was lower than using waterflooding. Combining shear rate and adsorption has a minimal impact on field indicators when compared to adsorption alone. This work enhances the comprehension of physical phenomena and non-Newtonian behavior in tertiary polymer flooding on heavy oil reservoirs and its impact on the production forecast. It also highlights important considerations for modeling-based procedures.

Numerical study of the impact of stress concentration on shale gas production

Predicting the production amounts has great significance for exploitation of oil and gas resources. The flow–geomechanical coupling effect plays an important role in predicting production. Shale rocks and the stress-dependent permeability model are critical for representing this coupling effect. The pore network of shale rock is not abundant like that of coal rock, and the matrix blocks of shale rock are not completely separated by the pore network. There is stress concentration around the pores when shale rock is deformed. Based on previous studies, the stress-dependent permeability model considering the impact of stress concentration is used in this study to build a numerical simulation model for flow–geomechanical coupling in a shale gas reservoir and validation using field data. Sensitivity studies are conducted to discuss the difference in production prediction between the new and common models. The main conclusions of this study are as follows: 1) The new model can fit both the field and experimental data well; the average error in daily production rate between the numerical solution and field data is 8%, which indicates that the new model can be used to predict shale gas production. 2) Under the geomechanical condition of a real reservoir, the difference in predicted production between models with and without stress concentration can be large and increase with the ratio of a to b. If this ratio is less than 50, the impact of stress concentration is not significant. Otherwise, the impact of stress concentration on production increases sharply and can exceed 20%. 3) The adsorption-induced strain enhances the impact of stress concentration on production. When the Langmuir pressure exceeds 1 MPa and Langmuir strain exceeds 0.01, the impact magnitude of stress concentration can exceed 10%.

A Case Study of Gas-Condensate Reservoir Performance with Gas Cycling

The study examines the application of dry gas injection technology (cycling process) in different depletion stages (25%, 50%, 75%, 100% of the initial reservoir pressure, and the dew point pressure) at a gas condensate field. The injection took place with varying numbers of injection wells relative to production wells (4:1, 3:1, 2:1, 1:1, and 1:2). The study assessed the impact of dry gas injection periods, ranging from 1 to 3 years, on increasing the condensate recovery factor in a real gas condensate reservoir named X. A hydrodynamic model was used and calibrated with historical data, resulting in a comprehensive approach. Compared to the traditional depletion development method, this approach led to a significant 9% rise in the condensate recovery factor. The results indicate that injection has a positive effect on enhancing the recovery factor of condensate and gas when compared to primary development methods based on depletion. As a result, these findings facilitate a rapid evaluation of the possibility of introducing similar measures in gas-condensate reservoirs in the future for reservoir systems that have a low and moderate potential for liquid hydrocarbons C5+. The optimised multidimensional hydrodynamic calculations, utilising geological and technological models, are crucial in determining the parameters for the technological production and injection wells.

Hydrogen storage and geo-methanation in a depleted underground hydrocarbon reservoir

AbstractCoupling of power-to-gas processes with underground gas storage could effectively allow surplus electricity to be stored for later use. Depleted hydrocarbon reservoirs could be used as stores, but practical experience of hydrogen storage in such sites is limited. Here we present data from a field trial that stored 119,353 m3 of hydrogen admixed to natural gas in a depleted hydrocarbon reservoir. After 285 days, hydrogen recovery was 84.3%, indicating the process’s technical feasibility. Additionally, we report that microbes mediated hydrogen conversion to methane. In laboratory experiments studying mesocosms that mimic real reservoirs, hydrogen and carbon dioxide were converted to methane (0.26 mmol l−1 h−1 evolution rate) reproducibly over 14 cycles in 357 days. This rate theoretically allows 114,648 m3 of methane per year to be produced in the test reservoir (equivalent to ~1.08 GWh). Our research demonstrates the efficiency of hydrogen storage and the importance of geo-methanation in depleted hydrocarbon reservoirs.

Influence of Imbibition Fracturing Fluid on the Original Water and Methane Occurrence in Actual Coalbed Methane Reservoirs Using the Integrated Device of Displacement and Low-Field Nuclear Magnetic Resonance.

The original water in the coal rock pores plays a controlling role in the occurrence of gas. Furthermore, during the hydraulic fracturing process, pressurized fracturing fluid with a higher pressure than the original pore pressure in the fractures drives the fracturing fluid to infiltrate into the coal rock pores, thereby altering the occurrence pattern of gas and water in the original pores. However, due to the limitations of the indoor simulation device, a systematic conclusion on the impact of the original pore water and imbibition fracturing fluid on coalbed methane reservoirs has not yet been formed. In this paper, an integrated device combining displacement and low-field nuclear magnetic resonance was employed using underground cylindrical coal rock samples as experimental subjects. Experimental conditions were maintained at a temperature of 30 °C, a confining pressure of 23 MPa, and an approximate reservoir pressure of 15 MPa. The initial water saturation levels were altered to 0, 27.88, and 42.18% to replicate the conditions of a coalbed methane reservoir at a depth of approximately 1200 m. Fracturing fluid with a pressure of 18 MPa was injected into the experimental samples to simulate the impact of the fracturing fluid on the original reservoir during hydraulic fracturing. This allowed for a realistic assessment of the influence of initial water saturation and fracturing fluid absorption on the coalbed methane recovery rate in the reservoir. The experimental results indicate that the imbibition process promotes the desorption of adsorbed gas, and the desorption amount of adsorbed gas increases with the increase in the original water saturation. This will result in an increase in the gas pressure within the pore system. The conditions of this experiment, in comparison to the previous ones, more closely resemble real reservoir conditions. This enables a realistic assessment of how the presence of the original water content and the absorption of the fracturing fluid affect gas occurrence within the reservoir.

Numerical study of gas invasion law in fractured reservoirs

Gas invasion during the drilling process in fractured reservoirs poses challenges, affecting drilling efficiency and increasing costs. Therefore, it is crucial to effectively and accurately describe the flow characteristics of subsurface fluids. Addressing the issue of gas invasion in fractured reservoirs, this study considers the influence of matrix deformation and fracture aperture variation on fluid flow and establishes a mathematical model for coupled flow and solid deformation in fractured reservoirs. The numerical formulation of the mathematical model is derived using the finite element method. To better represent real reservoir conditions, discrete fractures are created using MATLAB, and numerical solutions are obtained using the commercial software COMSOL Multiphysics. The accuracy of the model is verified through a comparison between numerical and analytical solutions. This paper first explores the characteristics of fluid flow within a single fracture and rock deformation when encountering a fracture during drilling. It then compares the predictive capability of the coupled model with that of the uncoupled model in estimating gas invasion. Finally, the primary factors influencing gas invasion in fractured reservoirs are analyzed from the perspectives of rock matrix, fractures, and drilling operations.

Numerical simulation of hydraulic fracture propagation after thermal shock in shale reservoir

The scale of propagation of hydraulic fractures in deep shale is closely related to the effect of stimulation. In general, the most common means of revealing hydraulic fracture propagation rules are laboratory hydraulic fracture physical simulation experiments and numerical simulation. However, the former is difficult to meet the real shale reservoir environment, and the latter research focuses mostly on fracturing technology and the interaction mechanism between hydraulic fractures and natural fractures, both of which do not consider the influence of temperature effect on hydraulic fracture propagation. In this paper, the hydraulic fracturing process is divided into two stages (thermal shock and hydraulic fracture propagation). Based on the cohesive zone method, a coupled simulation method for sequential fracturing of deep shale is proposed. The effects of different temperatures, thermal shock rates, and times on the scale of thermal fractures are analyzed. As well as the effects of horizontal stress difference and pumping displacement on the propagation rule of hydraulic fractures. The results show that the temperature difference and the thermal shock times determine the size and density of thermal fractures in the surrounding rock of the borehole, and the number of thermal fractures increases by 96.5% with the increase of temperature difference. Thermal fractures dominate the initiation direction and propagation path of hydraulic fractures. The main hydraulic fracture width can be increased by 150% and the length can be increased by 46.3% by increasing the displacement; the secondary fracture length can be increased by 148.7% by increasing the thermal shock times. This study can provide some inspiration for the development of deep shale by improving the complexity of hydraulic fractures.