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China boasts significant reserves of low-permeability oil reservoirs, and the economic and efficient development of these reservoirs plays a crucial role in enhancing oil and gas production. However, the “difficult injection and difficult recovery” issue in low-permeability oil reservoirs is a major challenge. To address this, research is conducted on the mechanism of pressure drive based on the mathematical model of oil-water seepage in low-permeability reservoirs and the model of fracture permeability. The study finds that pressure drive technology, by directly delivering the pressure drive agent deep into the low-permeability reservoir, effectively prevents viscosity loss and adhesion retention of the agent in the near-wellbore area. This technology expands the swept volume, improves oil washing efficiency, replenishes formation energy, and facilitates the gathering and production of scattered remaining oil. For reservoirs with higher permeability, pressure drive yields quick results, and high-pressure water injection can be directly adopted for pressure drive to reduce costs. On the other hand, reservoirs with lower permeability have difficulty in water absorption, and the use of surfactant-based pressure drive can effectively reduce the seepage resistance of the reservoir, enhancing its water absorption capacity and improving development outcomes. Based on the mechanism of pressure drive development, further research is conducted on the production characteristics of pressure drive mines. Addressing the variability in pressure drive effects, big data analysis tools such as SHAP analysis and correlation analysis are employed to evaluate the main controlling factors of pressure drive in both new and old areas. Additionally, non-time series and time series pressure drive production forecasting models are established based on pressure drive data.

This study investigates the feasibility of utilizing carbon dioxide (CO2) as a cushion gas in depleted reservoirs for enhanced gas storage efficiency and carbon sequestration against the backdrop of rising natural gas stable supply demand and climate change concerns. Simulations of gas storage reservoir scenarios require accurate dispersion parameters at flow alternation conditions to quantify the size of the miscible displacement front. Several experimental studies using core-flooding equipment aimed at measuring related parameters have been reported over the last decade but did not take flow alternation into consideration. We simulated directionally variable displacements to mimic the cyclic injection and extraction processes in gas storage, focusing on the dispersion characteristics of CO2 and methane (CH4) during flow alternation. Key findings were observed using Nuclear Magnetic Resonance (NMR) imaging, which provided real-time data on the spatial distribution and temporal changes of CH4 signals in rock cores. The results revealed that dispersion, influenced predominantly by dispersion coefficients rather than molecular diffusion, was significantly higher during alternating flow compared to concurrent displacement. Additionally, CO2 exhibited a greater dispersion effect when displacing CH4 than the reverse. This enhanced mixing efficiency during flow alternation supports the potential of CO2 as a cost-effective and efficient cushion gas, offering both improved storage performance and the added benefit of CO2 sequestration. These findings contribute valuable insights for the numerical simulation and operational adaptation of CO2 in gas storage reservoirs, emphasizing the importance of understanding fluid interactions under varying flow conditions to optimize storage efficiency and environmental benefits.

Amphiphilic-polymer flooding, which can increase water viscosity, decrease oil viscosity, and improve oil displacement efficiency, is a promising oil exploitation method for heavy oil. Due to oil–water emulsification, shear-thinning, and changes in oil viscosity when determining the relative permeability data of new amphiphilic polymers, the conventional J.B.N. method is not accurate. This paper presents a new method called the iso-permeability point trial method to determine the relative permeability curve by combining the J.B.N. method, the Corey model, and the relationship between water saturation and the relative permeability ratio. To avoid using polymer viscosity, a mathematical equation was derived based on the characteristics of the relative permeability curve. The results indicate that the new method is feasible and the obtained curve is more reasonable and smooth. The influence of concentration, permeability, and oil viscosity on amphiphilic-polymer displacement relative permeability was also analyzed, demonstrating that under the same water saturation, the water relative permeability is lower than that of water flooding but the oil relative permeability is bigger, which manifests as the iso-permeability point moves to the right and results in a lower residual oil saturation. In addition, the aforementioned trends are more obvious when the amphiphilic-polymer concentration is high, formation permeability is low, and oil viscosity is low.

Conventional numerical solutions and empirical formulae for predicting the initial productivity of oil wells in low-permeability reservoirs are limited to specific reservoirs and relatively simple scenarios. Moreover, the few influencing factors are less considered and the application model is more ideal. A productivity prediction method based on machine learning algorithms is established to improve the lack of application performance and incomplete coverage of traditional mathematical modelling for productivity prediction. A comprehensive analysis was conducted on the JY extra-low-permeability oilfield, considering its geological structure and various factors that may impact its extraction and production. The study collected 13 factors that influence the initial productivity of 181 wells. The Spearman correlation coefficient, ReliefF feature selection algorithm, and random forest selection algorithm were used in combination to rank the importance of these factors. The screening of seven main controlling factors was completed. The particle swarm optimization–extreme learning machine algorithm was adopted to construct the initial-productivity model. The primary control factors and the known initial productivity of 127 wells were used to train the model, which was then used to verify the initial productivity of the remaining 54 wells. In the particle swarm optimization–extreme learning machine (PSO-ELM) algorithm model, the root-mean-square error (RMSE) is 0.035 and the correlation factor (R2) is 0.905. Therefore, the PSO-ELM algorithm has a high accuracy and a fast computing speed in predicting the initial productivity. This approach will provide new insights into the development of initial-productivity predictions and contribute to the efficient production of low-permeability reservoirs.

During the past years, the recovery of unconventional gas formation has attracted lots of attention and achieved huge success. To produce gas from the low-permeable unconventional formations, hydraulic fracturing technology is essential and critical. In this paper, we present the development of a three-dimensional thermal-hydraulic-mechanical numerical simulator for the simulation of hydraulic fracturing operations in tight sandstone reservoirs. Our simulator is based on integrated finite difference (IFD) method, In this method, the simulation domain is subdivided into sub domains and the governing equations are integrated over a sub domain with flux terms expressed as an integral over the sub domain boundary using the divergence theorem. Our simulator conducts coupled thermal-hydraulic-mechanical simulation of the initiation and extension of hydraulic fractures. It also calculates the mass/heat transport of injected hydraulic fluids as well as proppants. Our simulator is able to handle anisotropic formations with multiple layers. Our simulator has been validated by comparing with an analytical solution as well as Ribeiro and Sharma model. Our model can simulate fracture spacing effect on fracture profile when combining IFD with Discontinuous Displacement Method (DDM).

CO 2 flooding recovery strongly depends on the minimum miscibility pressure (MMP). Conventional tests to determine gas–oil MMP such as rising bubble apparatus and slim tube displacement are either costly or time consuming. In order to propose a quick and accurate model to determine MMP, a back-propagation neural network is presented for MMP prediction during pure and impure CO 2 injections. Five new variables were screened as input parameters to the network. Next, the network was optimized using five evolutionary algorithms, and this work highlights that three of these evolutionary algorithms (e.g. Mind Evolutionary, Artificial Bee Colony, and Dragonfly) are firstly used to predict MMP. Then, data from the literature were input to the optimized network to train it. Statistical evaluation and graphical analyses were used to evaluate the performance of the proposed models and for comparison with published MMP correlates to obtain the optimal model for predicting MMP. The back-propagation model optimized using the dragonfly algorithm exhibited the highest accuracy among all those considered and MMP correlates; its coefficient of determination, average absolute percent relative error, root mean square error, and standard deviation were 0.965, 5.79%, 206.1, and 0.08, respectively. In addition, reservoir temperature was determined as the strongest MMP predictor (Pearson correlation = 0.63) based on sensitivity analysis.

An amount of oil remains in oil reservoirs even at the high water-cut stage of produced liquid from oil wells. To reveal the mechanism of displacement rates to affect the remaining oil in pore scales, a two-dimensional (2D) glass etching pore network model and real-time visual system were set up to observe the characteristics of oil distribution from water flooding and study the influence of displacement rates on oil recovery. It was found that the geometry of remaining oil in the pore network is diverse and dynamically changed at the high water-cut stage. Three geometric representative parameters were defined for the classification of five types of remaining oil (contiguous, branching, film, dropwise, bar columnar type), and controlling mechanisms for each type of remaining oil were analyzed. The experimental results show that the remaining oil saturation decreases from 21.2% to 6.5% when water injection rates increase from 0.05 to 0.5 mL/min. The increase in displacement rate improves the displacement efficiency of four types of remaining oil in the range of 55.00% to 93.67% except for dropwise type. The experimental data also indicate that the reduction in continuous residual oil and branched residual oil mainly contributes to the improvement of oil recovery of the whole network model. With the increase in displacement rate (from 0.05 to 0.1, 0.2, 0.3, 0.4, and 0.5 mL/min), the areas of five types of representative local residual oil reduce step by step. This research validates that the increase in water flooding rate in porous media leads to reduction in oil saturation, and it will improve oil recovery in oil reservoirs by enhancing water injection rates.

In consideration of the limited adaptability scope, low accuracy and high demand of great cost data of existent fracture prediction methods, a new fracture predicting method was advanced by implementing geological static data and production dynamic data from the Triassic Chang 63 reservoirs in the Huaqing Oilfield. Five constraints, lithology, sedimentary facies, thickness, rock rupture index and fracture intensity controlling the development of fractures were sorted out based on the static geological data. The multiple linear regression method was adopted to work out the quantitative relationships between the five constraints and fracture density, and the fracture density property of the whole area was calculated. Based on production dynamic data of well history, tracer, well interference test and intake profile test, the direction and distribution of fracture horizontally and vertically were figured out by reservoir engineering analysis method. The fracture density property was verified and quantitatively corrected with numerical simulation, and a 3D discrete fracture geological model in agreement with both geological cognition and dynamic production performance was built. The numerical simulation shows that the fracture model has higher fitting consistency, high reliability and adaptability. Key words: fractured oil reservoir, natural fracture, fracture geological model, fracture prediction, Huaqing Oilfield, Ordos Basin

Abstract: Based on the theory of non-Darcy seepage, a mathematical model for CO2 miscible and immiscible concurrent flooding considering changes of oil viscosity and threshold pressure gradient of oil and CO2 is established. A computational method of critical well spacing of CO2 miscible and immiscible concurrent flooding in ultra-low permeability reservoirs is deduced by solving the distribution of CO2 concentration in miscible flooding area with the mass transfer-diffusion-absorption equation and solving the saturation equation of immiscible affected area with characteristic line method. A critical well spacing example is built in the F142 and G89 reservoir blocks and the results show: (1) The critical well spacing increases with gas injection pressure, while decreases with gas injection speed; (2) The contribution of length in pure CO2 seepage area to the critical well spacing is the largest, the contribution of length in CO2-Oil effective mass transfer area and immiscible affected area is secondary, the contribution of pure oil area is the least, the gap of length between pure CO2 seepage area and CO2-Oil effective mass transfer area and immiscible affected area decreases with gas injection speed and it increases with the decreasing of gas injection speed, meanwhile, the law is more significant; (3) Pressure drop gradient of miscible affected area is significantly different from that of immiscible affected area and pressure drop gradient of CO2-Oil effective mass transfer area in miscible affected area is bigger than that of immiscible affected area. Key words: ultra-low permeability reservoirs, miscible flooding, immiscible flooding, critical well spacing, threshold pressure gradient

The fault block hydrocarbon reservoirs developed by water flooding in Shengli Oilfield have entered into the period of high water cut and low oil recovery. The residual oil to be recovered is challenging by using the unidirectional water or gas displacement method in producing oil from the reservoirs because of water invasion or gas channeling. A novel method of bidirectional displacement with artificial nitrogen gas cap and edge water is presented to improve the oilfield development recovery efficiency. This novel method refers to the nitrogen gas injection at the top of the inclined reservoir and the edge water injection at the bottom of the reservoir simultaneously. The experiments of the sandpack model are performed to explain the benefits of nitrogen gas flooding and to evaluate quantitatively the production performance of the bidirectional displacement process. A numerical simulation technique based on the proposed method is employed to find out befitting reservoirs for implementation of this new novel method. The X48 inclined fault block hydrocarbon reservoir in Dongxin production area, Shengli Oilfield, is selected as the actual research block to verify the benefits of bidirectional displacement with artificial nitrogen gas cap and edge water. The results indicate that the oil recovery by bidirectional displacement of nitrogen gas injection in the X4841 injection well and edge water injection in the X48CXN9 injection well cooperatively is higher than that of unidirectional water flooding or gas flooding in the X4841 injection well. The oil recovery factor increases by 11.2% than that by the original water injection development in the X48CXN9 injection well. Another interesting finding in this work was that, for the oil recovery, an appropriate ratio relationship is established between the nitrogen injection rate and the water injection rate regardless of production rate.

Abstract In this paper, we give the notion of locally convex probabilistic seminormed spaces and discuss some property of locally convex probabilistic seminormed spaces.

The inter-salt argillaceous dolomite reservoirs in the central region of China contain large abundance of oil resources with ultra-low permeability and porosity. However, the oil wells in this area show a very quick reduction with the decline of formation pressure. This article aims to investigate the main possible reasons that affect oil well productivity in the target oilfield. This study begins with analysis of capillary microscopic model, core stress sensitivity experiments, and non-Darcy percolation experiments. The impact of effective stress on permeability and porosity of the reservoir was revealed in this article. The novel productivity model and productivity evaluation model which couples stress sensitivity and threshold pressure gradient were proposed. The analysis of capillary microscopic model shows stress sensitivity of permeability to be much greater than that of porosity during the process of depressurization. The core stress sensitivity experiments results indicate that permeability and effective stress show index relationship while porosity and effective stress show binomial relationship. Damage rate and recovery rate of permeability and porosity were put forward to describe the degree of influence of stress sensitivity on permeability and porosity. The models were used to investigate the factors that affect single well productivity for the target oilfield. Application of the proposed model to this tight oilfield indicates that, the degree of influence of stress sensitivity is much greater than that of threshold pressure gradient. In addition, the greater the stress sensitivity coefficient and threshold pressure gradient are, the greater the productivity reduction will be.

The application of carbon dioxide (CO2) in enhanced oil recovery (EOR) has increased significantly, in which CO2 solubility in oil is a key parameter in predicting CO2 flooding performance. Hydrocarbons are the major constituents of oil, thus the focus of this work lies in investigating the solubility of CO2 in hydrocarbons. However, current experimental measurements are time-consuming, and equations of state can be computationally complex. To address these challenges, we developed an artificial intelligence-based model to predict the solubility of CO2 in hydrocarbons under varying conditions of temperature, pressure, molecular weight, and density. Using experimental data from previous studies, we trained and predicted the solubility using four machine learning models: support vector regression (SVR), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP). Among four models, the XGBoost model has the best predictive performance, with an R2 of 0.9838. Additionally, sensitivity analysis and evaluation of the relative impacts of each input parameter indicate that the prediction of CO2 solubility in hydrocarbons is most sensitive to pressure. Furthermore, our trained model was compared with existing models, demonstrating higher accuracy and applicability of our model. The developed machine learning-based model provides a more efficient and accurate approach for predicting CO2 solubility in hydrocarbons, which may contribute to the advancement of CO2-related applications in the petroleum industry. [ABSTRACT FROM AUTHOR]

In this paper, microscopic visualization devices and image processing techniques were used to conduct microscopic water flooding experiments. A novel classification criterion for residual oil during the water flooding process was established as follows: contiguous type, branched type, oil film type, columnar type, and droplet type. Correlations between the five types of residual oil saturation and water-cut were established. Also, the recovery improving potential for different production strategies have been investigated by water flooding experiments. The relationship between various types of residual oil saturation and injection velocity were obtained, with the former residual oil saturation decreasing to 16.09% during the injection process when the velocity was increased from 0.05 mL/min to 0.5 mL/min. However, with an increase in injection velocity, the saturation of contiguous residual oil type decreased first and then stabilized. The saturation of branched oil type monotonically decreased, but saturation of droplet oil type monotonically increased. Residual oil saturations were 22.62% and 9.69% before and after altering injection direction, respectively, which means residual oil recovery was increased by 12.93%. Some places were considered to be ''dead oil regions'' before altering injection direction with most of the contiguous and branched type residual oil in these areas been displaced out by altering injection direction. Altering the injection direction is a more effective method of enhancing oil recovery than improving injection velocity when the same volume of water is injected. The proposed method and prediction correlations were presented in this paper to provide a novel means to predict oil recovery and residual oil saturation types for water flooding reservoirs, which have strong plane and weak vertical heterogeneity. [ABSTRACT FROM AUTHOR]

The coning problems for vertical wells and the ridging problems for horizontal wells are very difficult to solve by conventional methods during oil production from reservoirs with bottom water drives. If oil in a reservoir is too heavy to follow Darcy's law, the problems may become more complicated for the non-Newtonian properties of heavy oil and its rheology. To solve these problems, an innovative completion design with downhole water sink was presented by dual-completion in oil and water columns with a packer separating the two completions for vertical wells or dual-horizontal wells. The design made it feasible that oil is produced from the formation above the oil water contact (OWC) and water is produced from the formation below the OWC, respectively. To predict quantitatively the production performances of production well using the completion design, a new improved mathematical model considering non-Newtonian properties of oil was presented and a numerical simulator was developed. A series of runs of an oil well was employed to find out the best perforation segment and the fittest production rates from the formations above and below OWC. The study shows that the design is effective for heavy oil reservoir with bottom water though it cannot completely eliminate the water cone formed before using the design. It is a discovery that the design is more favorable for new wells and the best perforation site for water sink (Sink 2) is located at the upper 1/3 of the formation below OWC. [ABSTRACT FROM AUTHOR]

Abstract  Fines release and migration is a universal problem in the production of oil from poorly consolidated sandstone reservoirs. This problem can result in the changes of porosity and permeability. It may not only damage a production facility, but it can also have a profound effect on oil recovery, resulting from the change in heterogeneity of the oil formation. Based on the macroscopic continuous porous media, continuity equations for multiphase flow in oil formations, and the theories of fines release and migration, a three-dimensional (3D) field scale mathematical model describing migration of fines in porous media is developed. The model is solved by a finite-difference method and the line successive over relaxation (LSOR) technique. A numerical simulator is written in Fortran 90 and it can be used to predict (1) the ratio of fines to production liquid volume, (2) the permeability change caused by colloidal and hydrodynamic forces resulting from fines release and migration, and (3) production performance. The numerical results of the one-dimensional model were verified by the data obtained by core displacement experiments. The sensitivity of numerical results with grid block size was studied by coarse grids, moderate grids, and fine grids. In addition, an oil field example with five-spot patterns was made on the numerical simulator. The results show that fines migration in an oil formation can accelerate the development of heterogeneity of the reservoir rock, and has an obvious influence on production performance, i.e., water drive front, water-cut trends, and oil recovery. [ABSTRACT FROM AUTHOR]