Cumulative Oil Production Research Articles

Energy-constrained production optimization for the in-situ conversion process of oil shale based on deep learning algorithms

The in-situ conversion process (ICP) involves complicated thermal-reactive-compositional flow processes where the production parameters are closely interdependent. Therefore, manual optimization of the production parameters of ICP through numerical simulation is arduous and time-consuming. This paper introduces a computational framework that integrates deep learning techniques and particle swarm optimization (PSO) to automatically optimize the values of production parameters of ICP, and thus locating the highest energy efficiency and the optimum energy usage. This approach utilizes a 3D CNN model to predict key metrics. The prediction process considers the detailed 3D heterogeneity of oil shale, resulting in remarkably high prediction accuracy, as evidenced by determination coefficients (R2) above 0.99. Subsequently, the trained CNN model is integrated to the PSO algorithm to automatically fine-tune the production parameters to optimize the energy efficiency of ICP.The optimization results yield three significant findings. First, as the energy consumption limit increases, the optimal number of heaters, well spacing, and heating temperature exhibit an upward trend, but stabilized beyond the threshold of 7 × 105 kWh. Secondly, the optimal input energy (7 × 105 kWh) is found for the given ICP model. Lastly, the analysis reveals that variations in initial reservoir pressure or bottomhole pressure have limited impact on cumulative oil production and energy usage.

Fracture prediction and application in carbonate reservoirs based on the rescaled range analysis- finite difference numerical analysis methods

For carbonate reservoirs, the study of fractures is crucial. In this paper, the identification and prediction of fractures in carbonate reservoirs are carried out by applying R/S-FD numerical analysis method by using core, thin section and imaging logging data. The analysis concludes that the AC, CAL, DEN, and GR curves are very sensitive to the response of fracture development segments as calculated by the rescaled range analysis (R/S) analysis method, but some fractures are still misidentified. After finite difference (FD) calculation on the basis of R/S calculation, high-angle structural fractures can be accurately identified, and the R/S-FD fracture identification results of different curves can be verified with the results of core and imaging logging, and the fracture identification accuracy can reach 71.69%. The number of fracture development segments is positively correlated with the cumulative oil production, the number of producing layers and the cumulative thickness of producing layers, and the wells with more fracture development segments have higher cumulative oil production, more producing layers and larger cumulative thickness of producing layers. This method improves the accuracy of fracture identification in carbonate reservoirs, which is of great significance for oil and gas exploration.

Research of numerical indicators for the development of the Asselskaya area of Orenburg oil and gas condensate field using the material balance method

The purpose of this work is to analyze and forecast the development indicators of the Assel deposit of Orenburg oil and gas condensate field. To complete this task, a large amount of data is required, which was obtained from the technological development project. The calculation is performed using a program written in the Python programming language. The variables for the material balance equation are given, some of them are calculated using intermediate formulas. The average values of the parameters over the last 15 years of development were chosen as the optimized parameters, since small amounts of cumulative production in the first years of development can lead to a significant error in the calculation of the material balance equation. Also, a comparison was made of the estimated forecast for the development of the Assel deposit with the forecast, according to the state plan, presented in the field development project. The comparison was made on the main parameters: cumulative oil production, annual oil production, oil recovery factor and water cut. For a visual comparison of the calculated parameters, dependency graphs are presented that reflect the forecast made by the material balance method, as well as the forecast based on the data of the state plan. The difference in the behavior of the curves shown on the graphs can be explained by the inaccuracy of the parameters describing the reservoir, as well as the inaccuracy of determining the initial recoverable reserves. This is also affected by the difference in reservoir drawdowns for injection and production wells, proposed in the state plan and in the forecast. Of course, the inaccuracy of the injectivity and productivity coefficients of wells, which were selected based on the estimated volumes of water injection and oil production, also affects. Based on the calculation performed, it can be concluded that it is expedient to further exploit the Asselskaya area of Orenburg oil and gas condensate field with the introduction of a reservoir pressure maintenance system until 2079. According to the forecasts, the water cut equal to 96% will be achieved in 2079, while the oil recovery factor will be 0.427.

Effects of forced fluid production intensity on oil rates and cumulative oil production in different producing intervals of the Romashkinskoye oil field

Effects of forced fluid production intensity on oil rates and cumulative oil production in different producing intervals of the Romashkinskoye oil field

Optimization of CO2 EOR and geological sequestration in high-water cut oil reservoirs

In terms of the collaborative optimization of CO2 flooding for Enhanced Oil Recovery (EOR) and CO2 sequestration, previous studies have co-optimized both cumulative oil production and CO2 sequestration by various algorithms. However, these solutions fail to optimize the CO2 injection schemes for high-water cut oil reservoirs. This paper presents an optimization methodology for CO2 flooding and sequestration in high-water cut oil reservoirs. The production optimization was carried out by adjusting the injection and production rate. To solve the proposed objective functions, the simultaneous perturbation stochastic approximation (SPSA) algorithm is applied in this paper, and the CMG-GEM module is utilized to simulate the reservoir production performance. A typical high-water cut reservoir in the Shengli oilfield was used to verify the feasibility of the presented methodology. In this paper, the production performance and net present value (NPV) for continuous gas injection under different water cuts were analyzed. The optimal timing of transforming from water flooding to gas displacement for the high-water cut reservoir was optimized. In addition, the optimal water–gas ratios for Water-Alternating-Gas (WAG) flooding were determined. The sensitivity of NPV to gas injection price and carbon subsidy was analyzed. The results show that when the gas price is 0.178 $/m3 and the carbon subsidy is 0.0169 $/m3, the optimal timing of transforming from water flooding to gas injection should be earlier than the time when the water cut is 0.82. Through the combination of NPV, cumulative oil production rate, and CO2 sequestration volume for WAG flooding, the optimal WAG ratio should be 1:2. The presented method in this paper considers various economic indicators and can optimize CO2 flooding and sequestration in high-water cut oil reservoirs efficiently, which can provide some guidance for the design of CO2 flooding schemes in high-water cut oil reservoirs.

Application of Machine Learning and Optimization of Oil Recovery and CO2 Sequestration in the Tight Oil Reservoir

Summary In recent years, shale and tight reservoirs have become an essential source of hydrocarbon production since advanced multistage and horizontal drilling techniques were developed. Tight oil reservoirs contain huge oil reserves but suffer from low recovery factors. For tight oil reservoirs, CO2-water alternating gas (CO2-WAG) is one of the preferred tertiary methods to enhance the overall cumulative oil production while also sequestering significant amounts of injected CO2. However, the evaluation of CO2-WAG is strongly dependent on the injection parameters, which renders numerical simulations computationally expensive. In this study, a novel approach has been developed that utilized machine learning (ML)-assisted computational workflow in optimizing a CO2-WAG project for a low-permeability oil reservoir considering both hydrocarbon recovery and CO2 storage efficacies. To make the predictive model more robust, two distinct proxy models—multilayered neural network (MLNN) models coupled with particle swarm optimization (PSO) and genetic algorithms (GAs)—were trained and optimized to forecast the cumulative oil production and CO2 storage. Later, the optimized results from the two algorithms were compared. The optimized workflow was used to maximize the predefined objective function. For this purpose, a field-scaled numerical simulation model of the Changqing Huang 3 tight oil reservoir was constructed. By December 2060, the base case predicts a cumulative oil production of 0.368 million barrels (MMbbl) of oil, while the MLNN-PSO and MLNN-GA forecast 0.389 MMbbl and 0.385 MMbbl, respectively. As compared with the base case (USD 150.5 million), MLNN-PSO and MLNN-GA predicted a further increase in the oil recovery factor by USD 159.2 million and USD 157.6 million, respectively. In addition, the base case predicts a CO2 storage amount of 1.09×105 tons, whereas the estimates from MLNN-PSO and MLNN-GA are 1.26×105 tons and 1.21×105 tons, respectively. Compared with the base case, CO2 storage for the MLNN-PSO and MLNN-GA increased by 15.5% and 11%, respectively. In terms of the performance analysis of the two algorithms, both showed remarkable performance. PSO-developed proxies were 16 times faster and GA proxies were 10 times faster as compared with the reservoir simulation in finding the optimal solution. The developed optimization workflow is extremely efficient and computationally robust. The experiences and lessons will provide valuable insights into the decision-making process and in optimizing the Changqing Huang 3 low-permeability oil reservoir.

The impact of well spacing on three-dimensional in situ extraction of oil shale using high-temperature air injection

To explore the impact of well spacing on the in situ high-temperature air injection for three-dimensional shale oil recovery, this study constructed six different well-spacing physical models of shale oil for investigation. By comparing factors such as temperature distribution, cumulative oil production volume, heating rate factor, and specific energy consumption per unit volume of shale oil among the six different well-spacing models, the influence of well spacing on the in situ high-temperature air injection for three-dimensional shale oil recovery was analyzed. The results showed that all six shale oil models exhibited a ripple pattern, advancing along the fractured fissures from the heating well to the production well. Model I, Model II, Model III, Model IV, Model V, and Model VI completed the full decomposition of the shale region at 2610, 1530, 1800, 3510, 4050, and 6660 days, respectively. The heating rate factor reached its peak at 1800, 900, 1440, 2340, 3240, and 3960 days. Model III had the lowest specific energy consumption per unit volume of shale oil, which was 905.41 kW/m3. Using Model III for three-dimensional shale oil injection with high-temperature air in situ recovery will shorten the heating time and reduce heat loss.

Study on Interpretation Method of Multistage Fracture Tracer Flowback Curve in Tight Oil Reservoirs.

Multistage fracturing is widely used in the development of tight oil reservoirs, and the fine description of postfracturing fracture networks is a challenge in tight oil reservoir development. Based on the formation mechanism of dual-wing fractures and the principles of tracer flowback, a mathematical model for tracer concentration in dual-wing fractures is established by considering the convective diffusion of the tracer within the fractures. An interpretation method for tracer flowback curves, utilizing a combination of Gaussian fitting and theoretical equation inversion, is developed to provide a detailed description of fracture parameters such as fracture half-length, fracture width, and fracture conductivity in the postfracturing fracture network. This method can be rapidly applied in field practices. Application examples demonstrate that the relative errors between the calculated cumulative oil and water production using this method and the actual data are less than 5%, validating the accuracy and applicability of the established mathematical model for tracer flowback and the interpretation method for tracer concentration curves in addressing practical problems.

A Comprehensive Evaluation of Shale Oil Reservoir Quality

To enhance the accuracy of the comprehensive evaluation of reservoir quality in shale oil fractured horizontal wells, the Pearson correlation analysis method was employed to study the correlations between geological parameters and their relationship with production. Through principal component analysis, the original factors were linearly combined into principal components with clear and specific physical meanings, aiming to eliminate correlations among factors. Furthermore, Gaussian membership functions were applied to delineate fuzzy levels, and the entropy weight method was used to determine the weights of principal components, establishing a fuzzy comprehensive evaluation model for reservoir quality. Without using principal component analysis, the correlation coefficient between production and evaluation results for the 40 wells in the Cangdong shale oil field was only 0.7609. However, after applying principal component analysis, the correlation coefficient increased to 0.9132. Field application demonstrated that the average prediction accuracy for the cumulative oil production per kilometer of fractured length over 12 months for the 10 applied wells was 91.8%. The proposed comprehensive evaluation method for reservoir quality can guide the assessment of reservoir quality in shale oil horizontal wells.

Simulation and performance prediction of partially naturally fractured reservoirs under solution gas drive primary recovery and gas injection processes

The study presented in this paper has multiple objectives. First, a simulation model for partially naturally fractured reservoirs under solution gas drive is developed. The model considers the major key parameters controlling fluid flow in the reservoir, including fracture intensity and distribution, instantaneous gas/oil segregation due to vertical capillary continuity, gas/oil gravity drainage, and reinfiltration of the drained oil to the lower matrix. Once the model is well-established, it is used to study the reservoir performance under two recovery processes: primary depletion and gas injection. A detailed investigation of the sensitivity of the ultimate oil recovery to the fracture intensity, oil production rates, and gas injection rates is performed. The findings of this study indicate that the ultimate oil recovery of low-fracture intensity reservoirs subjected to the depletion drive process is insensitive to production rates. However, for moderate- to high-fracture intensity reservoirs and low production rates, the recovery increases with increasing fracture intensity. Conversely, for moderate- to high-fracture intensity reservoirs and high production rates, the recovery is not significantly affected. For the gas injection mechanism, it is found that the ultimate oil recovery is a function of both the fracture intensity and gas injection rate. Furthermore, three fracture intensity ranges are identified: low, medium, and high. For the low- and high-fracture intensity ranges, the recovery increases with increasing gas injection rates and fracture intensity. However, for the medium fracture intensity ranges, the recovery behaves differently. It increases at low gas injection rates and decreases at high injection rates as the fracture intensity increases. New equations relating the cumulative oil production to the production rates, gas injection rates, and fracture intensity are also presented.

Increasing Oil Recovery by Gas Injection for Libyan Carbonate Sedimentary Field (LCSF) by using Eclipse Software

In this study, two software MBAL - Petroleum Experts and Eclipse are used to do comprehensive reservoir study for LCSF plane of development, this study covered analyses and evaluation. Gas injection essentially increases the rate of oil field development and in many cases permits increased oil recovery. This paper demonstrates a successful simulation case study based on a field data of a project. The objective of this study is to improve recovery from Libyan Carbonate Sedimentary Field by three wells of gas injection. To do that, first, the simulation 3-D model was built by using advanced reservoir simulation software (Schlumberger Eclipse). Second, select the best zone for gas injection. Third, select the best location for injector well. Fourth, determine the injector well depth. The results of the paper can be seen to match the real data of the reservoir with the results of the program using a MBAL software. The simulator results show the reservoir pressure history curve is matching to the stimulation curve, this gives a good allusion of the input data that has been entered to the model. The driving mechanism of this reservoirs it comes from three natural forces, which are fluid expansion, PV compressibility, and water influx. Gas injection scenario has a good plateau bpd lasts approximately 3 years and after that started to decrease. The Cumulative oil production is 108442340 STB barrels of oil with the recovery factor approximately 0.52805 and final reservoir pressure is maintained 328.76 pisa

Optimization of high-temperature gas parameters and analysis of the impact of oil shale thickness on the in situ development process

In situ shale oil extraction is an efficient and environmentally friendly development method. This article employs in situ extraction technology to heat shale oil, optimizing its high-temperature gas parameters based on indicators such as cumulative oil production rate and cumulative power consumption in the shale oil region. Using the optimized high-temperature gas parameters, an exploration of shale oil with different thicknesses was conducted. The results indicate that out of nine different high-temperature gas parameters, five cannot induce complete fracturing in the shale oil region. Models II, III, I, and IV each reach the onset of shale oil fracturing in the entire region after 870, 900, 1020, and 1080 days, respectively. The heating rate factor peaks at 1620, 1620, 1800, and 1980 days, with values of 0.277, 0.305, 0.317, and 0.32, respectively. Compared to Model II, the peak of the heating rate factor in Model I is the highest, showing an increase of 15.52%. Among these models, Model I has the lowest unit volume power consumption at 1063.41 kW/m3. The unit volume power consumption in Model I exhibits the most significant reduction, with a maximum decrease of 16.45% compared to the other three shale oil models of different thicknesses. The research results provide a basis for the engineering development of shale oil.

Advancing Reservoir Performance Optimization through UserFriendly Excel VBA Software Development

This study addresses the pressing demand for streamlined field performance analysis within oil and natural gas development, which currently necessitates substantial expertise and time investment. The principal aim involves developing a user-friendly software tool dedicated to optimizing reservoir rendition. Leveraging the Havlena and Odeh material balance straight line equation form, this tool integrates a zero-dimensional reservoir model with Decline Curve Analysis. The implementation of this user-friendly software enables achievable material balance optimization by aligning cumulative produced fluid with historical production data, akin to the widely acknowledged concept of history matching in material balance analysis. This accomplishment not only facilitates further endeavors like pressure simulation and forecasting but also augments the comprehension of reservoir dynamics. The analysis incorporated three datasets: one modeled from L.P. Dake's textbook and two drawn from real-life reservoirs in the Niger Delta. Assessment of estimated water influx and cumulative oil production indicated minimal discrepancies between Np Real and Np model for these reservoirs. Consequently, material balance history matching for these reservoirs seems feasible. Achieving reservoir rendition optimization involved a Microsoft Excel VBA code consisting of two hundred and thirty-five (235) lines, meticulously designed to replicate MBAL functionality. The software demonstrated congruent outcomes with MBAL, affirming its reliability for history matching and enhancing reservoir performance. We strongly advocate the utilization of this software for optimizing reservoir performance across diverse global regions. Its capacity to streamline field analysis could significantly benefit the oil and natural gas industry.

Horizontal well placement optimization based on matrix directional continuous element summation algorithm

During the course of actual oilfield development, judicious selection and design of well placement are paramount due to cost constraints and operating conditions. This paper introduces the Matrix Directional Continuous Elements Summation Algorithm (MDCESA), which is utilized to identify that segment with the largest summation for a given length in a 2D or a 3D matrix. An additional function that accounts for the distance between segments was added when searching for multiple segments to avoid intersections or overlaps between segments. The well placement optimization was transformed into a segment summation on a 3D matrix. Our findings reveal significant advancements in well placement optimization. Employing the MDCESA method, six producers were identified and their production performance was compared against two previously selected producers using a reservoir numerical simulator. The results demonstrated that the wells selected through MDCESA exhibited a substantial improvement in production efficiency. Specifically, there was an 11.6% increase in average cumulative oil production over a 15-year period compared to the wells selected by traditional methods. This research not only presents a significant leap in well placement optimization but also sets a foundation for further innovations in reservoir management and development strategies in offshore oilfields.

A Novel Model for Forecasting Production Performance in Waterflood Oil Reservoirs

The importance of production performance forecasting in reservoir development and economic evaluation cannot be overstated. Previous models have shown deficiencies in accurately predicting production performance, necessitating the development of a new semianalytical model to enhance its application scope and prediction accuracy. This study proposes a novel semianalytical model based on the Buckley–Leverett (BL) equation and a newly proposed linear relationship between outlet water saturation and average water saturation, as well as Willhite’s formula of oil/water relative permeability. The results demonstrate the universality of this new model, as it can generate three equivalent log‐linear relations, including the previously proposed model. Sensitivity analysis confirms the applicability of the model in various reservoirs. In addition, both model and field case studies highlight the advantages of this technique in forecasting water cut and cumulative oil production, with an extensive application scope covering over 90% of the water cut range. A comparison of oil production prediction results from six different predictive methods reveals that the proposed semianalytical model exhibits the lowest error rate of −0.01%. Moreover, the semianalytical model can be utilized to directly solve for the approximate values of the exponents in Willhite’s oil/water relative permeability equations. In summary, this novel semianalytical forecasting model demonstrates a robust ability to forecast water cut, cumulative oil production, and recovery efficiency.

The Impact of Autonomous Inflow Control Valve on Improved Oil Recovery in a Thin-Oil-Rim Reservoir

Summary Oil production from thin-oil-rim fields can be challenging as such fields are prone to gas coning. Excessive gas production from these fields results in poor production and recovery. Hence, these resources require advanced recovery methods to improve the oil recovery. One of the recovery methods that is widely used today is advanced inflow control technology such as autonomous inflow control valve (AICV). AICV restricts the inflow of gas in the zones where breakthrough occurs and may consequently improve the recovery from thin-oil-rim fields. This paper presents a performance analysis of AICVs, passive inflow control devices (ICDs), and sand screens based on the results from experiments and simulations. Single- and multiphase-flow experiments are performed with light oil, gas, and water at typical Troll field reservoir conditions (RCs). The obtained data from the experiments are the differential pressure across the device vs. the volume flow rate for the different phases. The results from the experiments confirm the significantly better ability of the AICV to restrict the production of gas, especially at higher gas volume fractions (GVFs). Near-well oil production from a thin-oil-rim field considering sand screens, AICV, and ICD completion is modeled. In this study, the simulation model is developed using the CMG simulator/STARS module. Completion of the well with AICVs reduces the cumulative gas production by 22.5% and 26.7% compared with ICDs and sand screens, respectively. The results also show that AICVs increase the cumulative oil production by 48.7% compared with using ICDs and sand screens. The simulation results confirm that the well completed with AICVs produces at a beneficial gas/oil ratio (GOR) for a longer time compared with the cases with ICDs and sand screens. The novelty of this work is the multiphase experiments of a new AICV and the implementation of the data in the simulator. A workflow for the simulation of AICV/ICD is proposed. The simulated results, which are based on the proposed workflow, agree with the experimental AICV performance results. As it is demonstrated in this work, deploying AICV in the most challenging light oil reservoirs with high GOR can be beneficial with respect to increased production and recovery.

Study on the limit of ineffective cycle control in Lamadian Oilfield in the late period of ultra-high water cut

The ineffective circulation in Lamadian oilfield is mainly banded ineffective circulation and dendritic ineffective circulation, and the ratio of ineffective circulation to the total well production is mostly concentrated below 40%. Therefore, this paper first divides the levels of ineffective circulation and simulates the proportion of liquid production in different levels of ineffective circulation to get the limits of ineffective circulation management. The results showed that the invalidation index R < 0.2 was a fourth-grade invalidation cycle, 0.2≤R < 0.3 was a third-grade invalidation cycle, 0.3≤R < 0.4 was a second-grade invalidation cycle, and R≥0.4 was a first-grade invalidation cycle. The reduction of water injection in the planar heterogeneous layer will lead to the decrease of cumulative oil production, and the discrimination index R is small, and the reduction range is almost unchanged. When the discriminant index R is above 0.4, the reduction of cumulative oil production increases significantly.

Raw hydrocarbons base of the republic of Uzbekistan - growth and production structure

The article considers the existing, actual raw material base of hydrocarbon raw materials of the Republic of Uzbekistan. Brief information is given on the number of discovered oil and gas fields, their distribution in the oil and gas regions of the republic, according to the degree of development, the type of fluids and the size of the reserves. Share participation of initial total resources in the context of oil and gas bearing regions is shown. Information is presented on the results of exploration work of two time periods - before 1991 and from 1991 to the present, where the growth rates of reserves of industrial categories for the first and second stages and the distribution of cumulative oil and gas production are considered. From the above data, it can be seen that the main increase in hydrocarbon reserves in the Republic of Uzbekistan was due to the discovery of unique and large fields in terms of hydrocarbon reserves. Based on the results of the analysis of the actual material, a con-clusion was made, indicating the high potential of the subsoil of the Republic of Uzbekistan and the feasibility of conducting exploration work in the long term, given the presence of significant forecast hydrocarbon resources. Keywords: field; oil; gas; hydrocarbons; reserves; oil and gas potential; initial total resources.

Waterflooding optimization based on the CRM and solving the linear programming problem

Among the many technologies for increasing the efficiency of oil field development using waterflooding, the least economically expensive include hydrodynamic methods for increasing oil recovery, based on changing the directions of filtration flows. Thus, one of the key tasks of increasing the efficiency of waterflooding at a late stage of development is to reduce the volume of ineffective injection. The article discusses the problem of redistributing volumes of injected water between injection wells for a given total injection volume in order to increase oil production. An analytical relationship is proposed for assessing injection efficiency, based on known displacement characteristics and calculated well interference coefficients based on CRMP. The optimization problem is formulated as a linear programming problem. Using the example of a fragment of an oil deposit, the possibility of assessing the injectivity of injection wells is shown, which makes it possible to increase the predicted oil production by changing the direction of filtration flows in the formation. As a result of applying the developed algorithm, the predicted increase in cumulative oil production amounted to 19683 m³ (9.5%) over 15 years. Keywords: waterflooding optimization; capacitive-resistive model; CRM; linear programming; injection efficiency; interwell connectivity.

Numerical Simulation Study of Pressure-Driven Water Injection and Optimization Development Schemes for Low-Permeability Reservoirs in the G Block of Daqing Oilfield

Pressure-driven water injection technology shows significant potential in addressing the key challenges of low-permeability oil reservoirs, improving water flooding development efficiency. Grounded in FDEM theory, this study establishes fluid matrix constitutive equations and employs FDEM to resolve rock stress–strain fields. A numerical simulation method for pressure-driven water injection in low-permeability reservoirs is developed to study the impact of different well pattern densities. The results indicate that the 90° horizontal well pattern using the five-spot method yields optimal outcomes, with approximately 32.32% higher cumulative liquid production than vertical well patterns. The 45° horizontal well pattern with the reversed nine-spot method also performs well, with about 30% higher cumulative liquid production than single-row vertical wells. Pressure-driven water injection improves matrix oil–water permeability and expands water flooding coverage. Based on the pressure gradient distribution driven by different well patterns, an evaluation method for the inter-well utilization capacity and its effectiveness was established. This method quantitatively assesses the reservoir depletion under various horizontal well encryption schemes. For research on timing of water injection in pressure-driven water flooding. Compared to pressure-driven water injection after 90 days, there is increased cumulative oil production after 40 days, emphasizing the importance of early pressure maintenance for higher cumulative oil production and enhanced recovery rates in low-permeability reservoir development. These findings provide crucial theoretical and practical support.