Search Results (347)

Remaining oil in high-water-cut reservoirs becomes increasingly dispersed during long-term waterflooding, while preferential flow paths cause severe ineffective water circulation and reduce the efficiency of further oil displacement. To improve the quantitative identification of remaining oil enrichment and water-flushed regions, this study proposes a displacement-unit-based classification and evaluation method for dominant remaining oil distribution. The method integrates dynamic allocation of injected water in multilayer reservoirs, time-varying characterization of reservoir physical properties, streamline-based delineation of displacement units, and saturation tracking using the φ-function. Two quantitative indicators, the remaining oil abundance index (I_so) and the water flushing intensity coefficient (C_f), were introduced to classify displacement units into strongly dominant, weakly dominant, and non-dominant types. The method was applied to a high-water-cut block of the W Oilfield, where 902 displacement units were identified from 65 oil and water wells and 36 sublayers. The results show that strongly dominant, weakly dominant, and non-dominant displacement units accounted for 37.9%, 33.7%, and 28.4% of the total, respectively. In 15 sublayers, the proportion of strongly dominant units exceeded 50%, indicating severe preferential water flow and limited remaining oil potential in these layers. Strongly dominant units were characterized by high water flushing intensity and low remaining oil abundance, whereas weakly dominant units showed remaining oil enrichment mainly at the margins of displacement units. The proposed method couples injection–production dynamics with seepage-field evolution and provides a quantitative basis for fine-scale adjustment of injection–production patterns in high-water-cut reservoirs. Full article

Numerical simulation is an effective method for studying groundwater flow and heat transfer in geothermal energy projects. Describing the characteristics of thermal plumes is important for operational planning of geothermal energy projects. In contrast to shallow geothermal system, the injection temperature differs significantly from the natural temperature of thermal reservoir in high-temperature geothermal projects, which leads to changes in fluid density and dynamics viscosity. The purpose of this paper is to investigate the impacts of temperature-induced changes in density and dynamics viscosity on simulation. The Enhanced Geothermal System (EGS) in North Jiangsu Basin, China, is taken as a case project. Based on the theory of groundwater flow and heat transfer in porous-fracture dual medium, a numerical model of EGS is established to predict the thermal performance. The density and the dynamics viscosity in the model were set as either constant or temperature-dependent to simulate the hydraulic head and temperature of the production well. The influence of temperature-induced changes in density and dynamics viscosity on the simulation was quantitatively studied. The results show that temperature-induced change in dynamics viscosity has a greater impact on the simulation, with deviation in hydraulic head exceeding 20% if the dynamics viscosity is assumed constant. The temperature-dependent variation in viscosity should be incorporated into the simulation process to improve the accuracy of the calculation. In practice, EGS projects should maximize the temperature differential between produced and injected water. The increased viscosity of lower-temperature circulation water extends its residence time within the system, thereby facilitating more thorough heat extraction. This research enhances our understanding of the role of the temperature in groundwater flow and heat transfer within EGS. Full article

Fracture–vuggy carbonate reservoirs contain discrete caves, fractures, conduits, and vugs, which makes recoverable-reserve evaluation strongly dependent on connected volume rather than on total pore volume alone. This study develops a unit-scale dynamic reserve-updating method for the S48 unit, Tahe Oilfield, by coupling a water-body-corrected material-balance equation, a heterogeneity-corrected waterflood characteristic curve, and iterative geological-model calibration. The main methodological contribution is to convert static fracture–vug architecture into dynamically constrained connected subsystems: the parameter Rwo quantifies connected/injected water volume at the fracture–vug unit scale, whereas the coefficient M corrects the apparent slope of waterflood curves for non-uniform sweep and preferential pathways. The revised workflow was calibrated against pressure, production, injection-response, and history-matched simulation data. Sensitivity analysis indicates that the estimated reserve-utilization degree increased from 48.77% +/− 4.8 percentage points during natural depletion to 74.1% +/− 6.7 percentage points after gas injection, reflecting staged reserve mobilization within the tested uncertainty range. The method is intended for field-scale reserve updating in reservoirs with sufficient pressure-production data; its transferability remains limited by static-model quality, channeling intensity, and the single-unit validation scope of this study. Full article

The physicochemical mechanisms and numerical characterization of amine-ether gemini surfactant emulsion flooding remain insufficient, limiting its field application in low-permeability reservoirs. This study developed a refined numerical simulation method that integrates full-process emulsion kinetics, including generation, coalescence, dispersion-assisted oil displacement, and demulsification, with graded emulsion characterization using the differentiated inaccessible pore volume (IPV) and residual resistance factor (RRF). Core-flooding validation demonstrated that the model accurately reproduced the key dynamic responses of water cut reduction and oil production increase, with a relative error of about 3.0%. Mechanistic analysis showed that the enhanced oil recovery performance arose from the combined effects of ultralow interfacial tension and emulsion-induced profile control. Relative to conventional surfactant flooding, emulsion flooding increased oil recovery by an additional 4.8–5.0% and lowered water cut by about 12 percentage points. For the Shengli Oilfield pilot block, the optimized injection design involved a surfactant concentration of 1.2 wt.%, an injection rate of 60 m³/d, a slug size of 0.01 PV, an injection–production ratio of 0.95, and a stepwise concentration-decline strategy. The field pilot further confirmed the applicability of the method: daily oil production of the well group increased by 46.5%, while comprehensive water cut decreased by 8.6 percentage points. These results demonstrate the value of the proposed method for both mechanistic characterization and field design of amine-ether gemini surfactant emulsion flooding in heterogeneous low-permeability reservoirs. Full article

The CO₂+O₂ in-situ leaching (ISL) mining process has been widely applied in the exploitation of sandstone-type uranium deposits; however, evaluating leaching efficiency remains a challenging issue. In this study, a sandstone-type ISL uranium deposit was selected, and based on comprehensive investigations of hydrogeological conditions and mineral geochemistry, a multi-physics coupled numerical model of uranium solute reactions during CO₂+O₂ leaching was established. The model fully accounts for variations in the groundwater flow field between injection and production wells and, on this basis, couples the chemical reaction field between the ore and the leaching solution. The model simulates the evolution of uranium concentration in the leaching solution and further calculates the leaching efficiency of the ore. The results indicate that groundwater flow velocity is highest between injection and production wells, where groundwater dynamics are strongest, and gradually decreases toward the interwell zones as hydrodynamic intensity weakens. Uranium concentration in the leaching solution is closely related to the groundwater flow field. In the early stage, high-uranium-concentration zones are mainly concentrated between injection and production wells. As time progresses, ore reactions in high-flow regions become more complete, leading to a decline in uranium concentration, while residual uranium ions within the formation diffuse outward under concentration gradients, causing high-concentration zones to expand outward. Sensitivity analysis shows that increasing CO₂ and O₂ concentrations significantly enhances uranium leaching concentrations, with increases of approximately 22.1% and 11.3%, respectively. Lower injection-production flow rates reduce dilution and promote more complete reactions, but may also introduce risks such as ore layer clogging. These results provide a theoretical basis and scientific guidance for flow-field regulation in situ leaching uranium mining. Full article

The capacitance resistance model (CRM) is an analytical tool widely used to forecast reservoir performance in enhanced oil recovery (EOR) methods. By representing flow dynamics and the connectivity between injection and production wells through the parameter of interwell connectivity, CRM offers fast computational processing and minimal input data requirements. These advantages make CRM a practical alternative for rapid reservoir analysis, especially when full-scale numerical simulations are infeasible due to time and budget constraints. CRM, rooted in material balance and productivity equations, uses injection/production rates and bottom-hole pressure data to construct reservoir models through optimization techniques, which can then be combined with oil fractional flow models for predictive purposes. Initially designed for waterflooding operations, CRM has seen limited but promising applications in gas injection projects, where research remains incomplete. This study presents a new formulation of CRM tailored for immiscible gas injection, incorporating the pseudo-pressure concept coupled with a saturation profile. The pseudo-pressure concept is a mathematical transformation that linearizes gas flow equations by accounting for variations in gas compressibility and viscosity with pressure. The proposed CRM was evaluated using a PUNQ-S3 benchmark reservoir model in the CMG IMEX black oil simulator, involving two injectors and four producers. History- matching results for fluid production rates showed that the newly developed CRM achieved the lowest NRMSE, outperforming other CRM models across a wide range of reservoir properties. Sensitivity analyses were conducted to examine the effects of gas and oil PVT properties on the model’s performance. The newly developed CRM, incorporating the pseudo-pressure concept and saturation profiles, demonstrates superior performance in predicting fluid production rates, achieving an average NRMSE of 15.3% in a base case scenario, compared to other tested CRM models. Additionally, the sensitivity analysis on the effect of fluid properties shows that higher gas viscosity, lower gas formation volume factor, and increasing oil API gravity improve the CRM model’s performance, and under all tested conditions the newly developed CRM provides the most accurate production history match. This study not only establishes the new CRM as a robust and accurate predictive tool for immiscible gas injection but also provides a comprehensive discussion on reservoir parameter ranges and model limitations, advancing the applicability of CRM in EOR processes. Full article

This study presents a wavelet-based framework for mapping inter-well connectivity (IWC) between multiple injectors and producers to support waterflood optimization. The method applies Cross-Wavelet Transform Coherence (CrWTC) with a complex Morlet wavelet to injection and production rate data, enabling the time-localized and frequency-dependent identification of dynamic injector–producer communication. The novelty of this work lies in continuous coherence mapping, the use of the complex Morlet wavelet for improved sensitivity to nonstationary responses, continuous updating as new data become available, and benchmarking on both the Volve and COSTA datasets. Validation using reservoir simulation and field data showed strong qualitative agreement with expected connectivity behavior and demonstrated clearer tracking of connectivity evolution and waterfront movement than the Capacitance Resistance Method (CRM). The proposed approach improves the reliability and interpretability of IWC assessment and offers a practical tool for reservoir surveillance and waterflood management. Full article

Inorganic scale formation in oil wells is a major flow assurance challenge, causing production losses, increased intervention costs and reduced operational efficiency. In Brazil, recent discoveries in pre-salt reservoirs have increased the relevance of calcium carbonate (CaCO₃) scaling under high-pressure and high-temperature (HPHT) conditions. Experimental data representative of petroleum environments under such conditions, particularly regarding the influence of CO₂ and flow conditions, remain limited. In this study, a compact pressurized experimental unit was designed and constructed to investigate the dynamic formation, deposition and adhesion of CaCO₃ under conditions close to those encountered in oil production systems. A dedicated experimental methodology was developed to promote controlled mixing of aqueous sodium bicarbonate (NaHCO₃) and calcium chloride (CaCl₂) solutions and CO₂ injection, enabling precise control of pressure, temperature and flow regime. The effects of turbulent flow, expressed by different Reynolds numbers, on the deposited CaCO₃ mass and its adhesion to the substrate were systematically evaluated under controlled conditions of 40 °C and a pressure drop of 15 bar was imposed in the control valve in order to promote the flash of CO₂ and CaCO₃ precipitation. Complementary characterization analyses were performed to assess crystal morphology and adhesion detachment strength. The results provide new experimental insights into CaCO₃ scaling mechanisms under CO₂-rich flowing conditions, contributing to improved understanding of scale adhesion and the development of mitigation strategies for flow assurance in oil and gas operations. Full article

Optimizing tillage and fertilization practices is of vital importance for enhancing soil carbon retention, improving soil quality and increasing crop productivity in the intensive wheat (Triticum aestivum L.)–maize (Zea mays L.) double cropping system (WM). However, the combined effects of subsoiling (ST) and liquid manure (LM) application on yield sustainability and the dynamic changes in labile organic carbon (LOC) fractions (LOCs) remain insufficiently quantified in WM in the North China Plain (NCP). A two-year field experiment evaluated the responses of grain yields, the sustainable yield index (SYI), soil organic carbon (SOC), LOCs, C pool management indexes (CPMIs), and the soil quality index (SQI) to both patterns of tillage [conventional shallow rotary tillage (RT) and ST] and fertilization [conventional fertilization (CF), LM broadcast (LMB), and LM injection (LMI)] in WM in the NCP. Compared with RT, ST significantly enhanced crop grain yields (3.5~4.1%) and the annual SYI (4.1%) (p < 0.05). The contents of SOC, total labile OC (TLOC), high LOC (HLOC), and medium LOC (MLOC) and the values of SQI were higher in soil layers at both 0–20 cm and 20–40 cm under ST than those under RT. Compared with CF, LMI significantly enhanced grain yields (5.8~6.1%) and the annual SYI (5.4%). LMI significantly increased the contents of SOC, TLOC, HLOC, and MLOC and the SQI values in both soil layers relative to CF, while no significant difference was observed for grain yields, the annual SYI, and the SQI between LMB and CF. The higher contents of SOC and LOC led to an increase in the values of CPMIs based on TLOC (TCPMI), HLOC (HCPMI), and MLOC (MCPMI). The combination of both ST and LMI enhanced SOC retention through the increase in recalcitrant organic carbon (ROC) content and the transformation process of LOCs. It was obvious that HLOC and MLOC affected SOC, HCPMI, and MCPMI in the soil layers at both 0–20 cm and 20–40 cm, and thus can be regarded as sensitive indicators reflecting the dynamic changes in SOC and soil quality. Therefore, the combination of subsoiling and liquid manure injection can promote labile OC transformation, SOC retention, soil quality, and yield sustainability, providing an effective management strategy for the achievement of sustained agricultural production in the NCP or other regions with similar conditions. Full article

To investigate the synergistic effect of hydraulic fracturing and hot water injection on enhancing methane extraction from low-permeability coalbeds and elucidate the underlying thermal-hydraulic coupling mechanism, methane desorption experiments were conducted in coal samples with varying fracture networks using a self-developed multi-field coupling experimental system. Tests were performed under different injection pressures and temperatures to analyze coal temperature evolution and methane desorption-seepage characteristics. The results demonstrate that hydraulic fracturing significantly improves pore structure and connectivity, thereby optimizing methane desorption behavior. The methane migration in the samples is influenced by water injection, exhibiting an initial promotion followed by inhibition. The combined fracturing-thermal injection approach effectively reduces the dynamic viscosity of water, mitigates the water lock effect, and enhances the desorption capacity. The hydraulic fracturing and the hot water injection complement each other, achieving synergistic production enhancement. The optimal injection pressure and water temperature can be selected according to specific reservoir conditions to balance the production increase and cost efficiency. This laboratory-scale study provides theoretical support for optimizing hydraulic measures and thermal injection techniques in coalbed methane extraction, revealing complementary synergies between these two methods and offering new insights into multi-field coupling enhancement mechanisms with practical application guidelines. Full article

Based on the waterflooding development practice of thick carbonate reservoirs in the Middle East, aiming at the practical problems such as complex water invasion types, rapid water breakthrough of oil wells and poor development performance in such reservoirs, this study takes the MB1 reservoir of H Oilfield as the research object and establishes a multi-scale hierarchical screening scheme for the main controlling factors of water cut rise covering the reservoir-block-well group levels. Firstly, the target reservoir is divided into several typical development blocks by means of numerical simulation technology. On this basis, the dynamic development characteristics of the reservoir, typical blocks and well groups are analyzed respectively. The multi-sequence grey correlation method is adopted to screen the common influencing factors of water cut rise in typical blocks, and then the multi-factor sensitivity analysis of the screened key factors is carried out by numerical simulation. Finally, it is determined that the main controlling factors affecting the water cut rise in the reservoir are the development degree of high-permeability layers, the rationality of well pattern layout, and the injection–production intensity, and the corresponding development adjustment strategies are proposed accordingly. Guided by the multi-scale hierarchical screening of main controlling factors for water cut rise, this study improves the traditional grey correlation method and proposes a multi-sequence grey correlation analysis method. This method for determining the controlling factors, which combines mathematical approaches with reservoir numerical simulation techniques, gives full play to the advantages of both. It reduces the range of variables in numerical simulation analysis, avoids the sharp increase in simulation complexity caused by multi-factor coupling, and greatly improves work efficiency while ensuring research depth. Full article

During offshore oilfield development, traditional injection–production processes commonly suffer from delayed regulation, low operational efficiency, and heavy reliance on manual intervention. Achieving real-time diagnosis of injection–production anomalies and dynamic optimization under complex geological conditions and harsh marine environments represents a core scientific challenge. This study presents the development and field deployment of an intelligent cable-controlled injection–production integrated management system. The work is positioned as an application- and system-oriented study, focusing on addressing practical challenges in offshore oilfield operations through the integration of established machine learning techniques into a cohesive operational platform. The system employs a cloud-native microservice architecture and integrates nine functional modules, enabling closed-loop management from data acquisition to intelligent decision making. Key methodological contributions include: (1) a weighted ensemble model combining Random Forest and SVM for blockage diagnosis, balancing global feature learning with boundary sample discrimination to achieve 92% diagnostic accuracy; (2) a Bayesian fusion framework that integrates static geological priors with dynamic sensitivity analysis for probabilistic quantification of injector–producer connectivity, achieving 85% identification accuracy with rigorous uncertainty propagation; and (3) a three-stage human–machine collaborative mechanism that substantially reduces anomaly response latency while ensuring field safety. Field application in Bohai oilfields demonstrates that the system shortens the injection–production response cycle by approximately 42%, reduces anomaly response time from over 72 h to less than 2 h (a 97% reduction), decreases water consumption per ton of oil by 27.6%, and increases injection–production uptime by 11.3 percentage points. This study provides an interpretable, extensible, and closed-loop technical solution for intelligent offshore oilfield development, with future directions including digital twin predictive simulation and reinforcement learning for real-time optimization. Full article

Machine learning methods have gained significant attention in forecasting waterflooding performance in recent years, but their accuracy often remains insufficient for practical field applications. This study proposes a hybrid framework that integrates a linear dynamical system (LDS) with a neural network (NN). The framework improves oil-rate prediction by decomposing the injection–production relationship into linear and nonlinear components. Specifically, the aggregate injection rate is approximately linearly related to total liquid production, which is effectively captured by the LDS model, based on reservoir material balance principles. In contrast, the oil fraction of the produced liquid, defined as the ratio of oil rate to liquid rate, is bounded between 0 and 1 and typically decreases over time. This nonlinear trend is accurately modeled using a neural network (NN). The parameters of the LDS–NN framework are learned from historical injection and production data via a supervised training process. Furthermore, key hyperparameters within the model can be adjusted to optimize the performance for different reservoir characteristics. The proposed hybrid method is evaluated using both simulated reservoir cases and real field data, and compared against the performance of LDS-only and NN-only models. The results demonstrate that the LDS–NN framework consistently provides more accurate oil-rate predictions than either standalone LDS or NN approaches, across both synthetic and real-world waterflooding scenarios. Full article

In water injection development of tight oil reservoirs (TORs), the complex fracture network formed by hydraulic fracturing and water injection induction is the key factor determining the development effectiveness. Accurate inversion of water injection-induced fracture parameters holds significant importance for enhancing reservoir development outcomes. This paper innovatively proposes a parameter inversion framework that integrates the Embedded Discrete Fracture Model (EDFM) with intelligent optimization algorithms. EDFM efficiently characterizes complex unstructured fracture systems while maintaining mass conservation between the matrix and fractures; intelligent optimization algorithms automatically invert parameters such as fracture half-length, orientation, and conductivity. First, a three-dimensional geological model of the TOR is constructed, utilizing EDFM to handle the impact of fractures on the seepage field. Based on considerations of fracture geometry, conductivity, and stress sensitivity, a coupled fluid dynamics model for fractures and matrix is developed. Subsequently, an objective function is built based on water injection production dynamic data, and the Projection-Iterative-Methods-based Optimizer (PIMO) algorithm is employed to achieve efficient inversion of fracture parameters. Taking a TOR in the Ordos Basin as an example for verification, through synthetic model validation, this method significantly improves the accuracy and efficiency of history matching, with inversion results reliably guiding numerical simulation predictions. The results demonstrate that this method can effectively enhance the precision of fracture parameter identification, offering clear advantages in inversion speed and accuracy over traditional trial-and-error approaches. This study provides new insights for modeling induced fractures in TORs and optimizing water injection development strategies. Full article

Addressing the severe risk of artificial fractures causing vertical pressure channeling and subsequent water flooding during shale oil development in the Ordos Basin, this study investigates the overlapping development zone in Block Shun 269. Through laboratory rock mechanics experiments, the mechanical anisotropy of the overlapping layers was characterized. Utilizing actual production data, a 4D dynamic geomechanical model incorporating 21 years of injection-production history was established to reconstruct the pre-fracturing 3D in situ stress field. Based on this stress field model, a quantitative analysis was conducted on the evolution of injection-production stresses, the vertical superposition distance, the distribution of natural fractures, and the propagation patterns of hydraulic fractures across layers under various fracturing engineering parameters (including pumping rate, fluid viscosity, and perforation cluster, etc.). Research indicates that long-term injection-production disturbances caused the average minimum horizontal principal stress in the Chang 6 layer to decrease by 1.6 MPa, with partial “stress deficit zones” experiencing reductions as high as 3.5 MPa. This significantly weakened the stress shading capability between layers, resulting in the probability of fracturing cracks through the Chang 7 layer in the lower section increasing from 12% to 49%. The propagation of fracture height is jointly governed by geological and engineering factors, the weighting order is as follows: superposition distance > pumping rate > interlayer stress difference. A fracturing cross-layer risk assessment chart based on the coupling of geological and engineering factors has been established, proposing different anti-leakage and fracture control technical models for fracturing sections with different risk levels. Using this model to simulate fracturing in B horizontal wells, the simulation results were consistent with microseismic measurement data. Full article