Search Results (3,137)

This study presents a risk assessment of asphaltene–resin–paraffin deposition (ARPD) in the producing formations of the XIII reservoir unit of the Uzen oil field at a late stage of development. The crude oil is characterized by an extremely high paraffin (wax) content of up to 29 wt.%. Long-term operation of the reservoir pressure maintenance (RPM) system with cold water injection has resulted in significant reservoir cooling, with temperatures declining from the initial 60–65 °C to 20–30 °C in zones of intensive waterflooding. To refine the critical phase transition temperatures of paraffin components, a dynamic laboratory approach was applied using a Wax Flow Loop system, which simulates wax deposition processes under flowing conditions. The results indicate that the wax appearance temperature (WAT) ranges from 41.0 to 44.0 °C, significantly exceeding the current bottomhole temperatures in the cooled zones of the reservoir. Intensive bulk crystallization of paraffins occurs within the temperature interval of 33.5–35.0 °C, while loss of oil flowability is observed at 25–34 °C, corresponding to the gelation and structural network formation of wax crystals under reduced thermal conditions. The obtained results confirm the inevitability of bulk oil structuring and solid wax phase precipitation directly within the reservoir porous medium. This process leads to blockage of low-permeability interlayers, deterioration of filtration properties, and a reduction in the displacement efficiency factor by 20–35%. Under the current thermal regime, ARPD should therefore be considered not merely as an operational flow assurance issue, but as a systemic factor limiting reservoir development efficiency. The research results substantiate the need to transition from reactive ARPD removal methods to proactive management of the thermal regime of the reservoir and wells, as well as to the differentiated application of thermal and chemical treatment methods. Full article

With the increasing complexity of reservoir formation mechanisms and the increasing difficulty of exploration, accurate reservoir prediction is critical for oil and gas exploration. However, traditional methods struggle to simultaneously achieve multi-source data fusion and spatial structure characterization. This study proposes a sequential stochastic fuzzy simulation (SSFS) method that integrates fuzzy recognition and sequential stochastic simulation to fuse well logging and seismic data while preserving geological spatial structure. In order to verify the effectiveness of the method, a tight sandstone reservoir in the D block of the Sulige gas field, Ordos Basin, was taken as the research target. Four gas-sensitive seismic attributes are selected, and the SSFS model is then constructed by fusing well–seismic multi-source data. Validation shows high consistency between predicted and measured gas thickness, with an R² of 0.955 and an RMSE of 0.866 m, consistent with the dynamic gas testing results of horizontal wells. Compared with conventional geostatistical and machine learning methods, the SSFS method achieves higher accuracy, stronger spatial rationality, and better generalization ability in blind-well validation. Uncertainty analysis (mean, SD, CV, P10-P50-P90) confirms low uncertainty and high reliability. Therefore, the proposed method is reliable and effective, providing new insights for reservoir prediction. Full article

As oil and gas reservoirs progress into the mid-to-late stages of development, produced fluids increasingly exhibit high water-cut and complex flow regimes. Conventional water-cut measurement techniques based on capacitance, conductance, and resistance often face challenges in terms of accuracy, stability, and adaptability. In this study, a novel non-contact broadband microwave system, based on a ridged-horn antenna microwave transmission sensor (RHAMTS), is proposed to achieve highly sensitive full-range (0–100%) water-cut monitoring. The RHAMTS consists of two identical ridged-horn antennas, whose geometries are optimized through analytical design calculations and full-wave finite-element simulations. Numerical simulations are first performed to elucidate the sensing mechanism. Subsequently, static and dynamic experiments are conducted under two representative conditions: emulsified oil-water mixtures and stratified oil-water layers. The results indicate that the broadband spectral signatures of the RHAMTS can effectively characterize water-cut in both emulsified mixtures and stratified oil-water layers. For emulsified mixtures, both amplitude attenuation and phase shift vary systematically with water-cut, and the RHAMTS can still effectively characterize water-cut under saline conditions. For stratified oil-water flow, results from both static and dynamic experiments demonstrate that amplitude attenuation provides more robust features for practical water-cut discrimination. Compared with conventional methods, the proposed RHAMTS offers non-contact operation, rich spectral information, and compatibility with various flow regimes, providing a feasible and efficient approach for water-cut monitoring under complex field conditions. Full article

Natural fracture development in tight-sand gas reservoirs is strongly controlled by tectonic evolution yet remains difficult to characterize using conventional seismic interpretation due to limited resolution. This study presents a damage-mechanics-based approach that integrates 2D seismic data, well logs, and drilling information to construct a 3D geological model and simulate tectonically induced fracture development under regional orogenic loading. The approach is applied to the Permian formation in the Ordos Basin. Modeled damage zones, interpreted as areas of enhanced natural fracture development, show strong spatial correspondence with high-productivity wells. The results demonstrate that damage mechanics provides an effective framework for linking tectonic processes with fracture distribution in tight-sand reservoirs and offers new insights into fracture-controlled gas accumulation and productivity. This case demonstrates the applicability and effectiveness of the technology of continuum damage mechanics for 3D natural fracture distribution based on sets of 2D seismic data plus drilling data. Although sets of 2D seismic data cannot replace real 3D seismic data for all its usage, it can produce numerical results of natural fractures with reasonable accuracy for calculation of natural fractures with damage mechanics method. 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

Phosphorus release from sediments significantly influences eutrophication in shallow lakes; however, its dynamics in drawdown zones under alternating inundation and drying cycles remain understudied. This study investigates the mechanisms of phosphorus release from sediments in the drawdown zone of Nansi Lake, a key reservoir along the eastern route of the South-to-North Water Diversion Project. Through field sampling and laboratory simulations, we analyzed the impact of inundation duration, physicochemical properties, and organic matter decomposition on phosphorus release. In Container a (first inundation period), phosphorus was rapidly released at the beginning of inundation, with total phosphorus (TP) in the overlying water increasing from 1.92 mg/L to 2.68 mg/L, and in the interstitial water from 8.45 mg/L to 15.24 mg/L. The second inundation period showed the highest phosphorus release, with TP reaching 3.61 mg/L in the overlying water and 21.51 mg/L in the interstitial water. Inorganic phosphorus dominated the release, with dissolved inorganic phosphorus (DIP) accounting for a higher proportion of TP than dissolved organic phosphorus (DOP). Changes in pH, oxidation-reduction potential (ORP), dissolved oxygen (DO), and total organic carbon (TOC) significantly influenced phosphorus distribution. The decomposition of organic matter during inundation increased dissolved organic matter levels, thereby affecting phosphorus release. These findings provide valuable insights into phosphorus dynamics and highlight the need for integrated management strategies to mitigate internal phosphorus loading and prevent eutrophication in Nansi Lake, offering guidance for water quality management and ecological protection in similar shallow lake systems. Full article

Hydrologic models are instrumental in understanding the behavior of the Nile River Basin (NRB), yet their effectiveness is often limited by the basin’s complex hydrology and sparse observational records. This study applies a basin-scale hydrological modeling approach to simulate near-natural, pre-reservoir flow conditions in the NRB, while incorporating lake and wetland submodels. The basin was discretized into 34 sub-watersheds with an outlet at Aswan. The conceptual GR4J rainfall–runoff model was implemented within the Raven modeling framework, chosen for its parsimony and suitability for data-limited conditions. Multi-objective calibration used discharge data from the Global Runoff Data Centre (GRDC), supplemented by digitized historical records to improve spatial and temporal coverage. A stepwise calibration strategy was applied at 18 sites, focusing on pre-reservoir periods to capture natural flow dynamics. The calibrated model reproduces observed discharges with high skill. At the Aswan outlet, Nash–Sutcliffe Efficiency (NSE) values were 0.87 (calibration) and 0.80 (validation), with percent bias (PBIAS) values of 6.1% and 5.0%, respectively. Model performance was strongest in the Blue Nile, White Nile headwaters, and the Nile main stem. The model also successfully simulated the hydrological step-change observed in Lake Victoria during the 1960s, underscoring its robustness in simulating regional hydroclimate disruptions. This calibrated model enables reconstruction of historical Nile discharge and simulation of past hydrologic disturbances, including those driven by major volcanic eruptions over the past millennia. Full article

Constant composition expansion (CCE) experiments provide critical relative-volume and density information describing the thermodynamic behavior of reservoir oils and gases under varying pressure. These properties are vital inputs for hydrocarbon reservoir engineering, as they impact how oil and gas move through the reservoir during production. However, the need for specialized personnel, high-end equipment and measures taken to ensure safety in handling high pressure fluids often render the CCE experiments expensive and slow. This work introduces a Local Interpolation Method (LIM), a proximity-informed, end-to-end CCE fluid properties prediction Artificial Intelligence (AI) model that leverages domain expertise and synthetic Pressure–Volume–Temperature (PVT) data archives that mimics the actual data. The AI model generates surrogate CCE behavior for new fluids, thereby reducing the need for completing laboratory CCE measurements when sufficiently similar fluids exist in the available archive and neighborhood support is strong. Each new fluid is embedded in a compositional–thermodynamic descriptor space, and its response is inferred from a small neighborhood of thermodynamically similar fluids. Within this locality, the LIM combines hybrid local interpolation for key scalar properties (such as saturation-point quantities and expansion endpoints) with shape-preserving reconstruction of monophasic and diphasic relative-volume curves, enforcing continuity at saturation and consistency between relative volume, density and compressibility. The workflow operates purely at inference time and does not require case-specific retraining. Application to a curated archive of CCE tests shows that LIM reproduces key CCE features with very good agreement to existing data across a range of fluid types, indicating that proximity-based AI modeling can substantially reduce reliance on new CCE experiments while maintaining engineering-useful agreement for compositional simulation workflows. Under leave-one-out evaluation on 488 CCE tests, mean curve-level Mean Absolute Percentage Error (MAPE) is 0.07% for monophasic relative volume and 0.07% for monophasic density. For well-supported neighborhoods (Tiers 1–3, n = 376), mean MAPE is 0.04% for both, with 2.65% for derived compressibility and 1.78% for diphasic relative volume. The workflow is automated in software to facilitate reproducible inference on operator-owned archives and can reduce turnaround time and laboratory burden in well-supported neighborhoods. The proposed AI model uses available experimental data owned by each operator and does not use others’ data while respecting the data privacy and data ownership. Full article

Accurate prediction of subsurface temperature distributions is essential for geothermal reservoir assessment, thermal performance evaluation, and decision support in reservoir management. However, repeated high-resolution numerical simulations are computationally expensive, particularly when multiple scenarios, heterogeneous petrophysical fields, and varying grid resolutions must be analyzed. This study presents a U-Net-based surrogate modeling framework for fast geothermal temperature field prediction on structured grids, coupled with interpolation strategies for handling unseen grid resolutions and intermediate time instances. Training and evaluation data are generated using the MATLAB Reservoir Simulation Toolbox (MRST) (24.1.0.2578822 (R2024a) Update 2) under multiple porosity–permeability realizations and at several grid resolutions (130 × 73, 67 × 37, 36 × 19, and 20 × 11) on a 2D grid. Data preprocessing and reshaping techniques are used to preserve spatial correspondence across resolutions. For fixed trained grids, the surrogate directly predicts temperature fields from porosity, permeability, and time inputs. For unseen grids, a grid interpolation strategy combines predictions from neighboring trained resolutions using weighted blending based on target grid cell count, followed by spatial resizing to the requested resolution. In addition, time interpolation is used to estimate temperature maps at intermediate time steps between predicted/simulated snapshots. The proposed framework enables rapid generation of temperature maps while maintaining spatial structure, making it suitable for efficient geothermal screening and multiscale scenario analysis. Full article

Defining the near-surface signal reflecting the deep sub-surface leakage is a critical challenge in the risk assessment of geologic carbon storage (GCS) projects, often exacerbated by decoupled deep-to-shallow modeling. This study quantifies the mass distribution and phase evolution of leaked CO₂ through deep reservoir-caprocks, intermediate aquifer, and near-surface soil, thereby showing the sub-surface retention characteristics and the detectability of near-surface signals. A geological model from the deep reservoir to the soil layer was constructed to simulate CO₂ leakage through the caprock and migration into overlying strata in 1000 years. Using the simulator of GPSFLOW, this study evaluates the evolution of fluid phases and the mass distribution during the injection for 100 years and the post-injection periods. The results indicate that (1) at the moment the injection ceases, 87.43–99.06% of the CO₂ remaining within the system is retained within the reservoirs, with less than 8.42% reaching the intermediate aquifer. Remarkably, although the CO₂ ultimately reaching the near-surface soil is less than 0.00073% of the total mass retained within the system, this mass accumulation translates to a concentration anomaly with a signal-to-noise ratio of 368 relative to the background baseline. (2) Sensitivity analysis reveals that the injection rate affects the timing of fluid transport—a tenfold increase in injection rate (from 3.17 to 31.7 kg/s) accelerates the upward movement of CO₂, advancing its arrival at the near-surface by 15 years without changing the overall mass partitioning. The permeability anisotropy ratio affects CO₂ migration and phase distribution—decreasing the vertical to horizontal permeability ratio (1, 0.5, 0.25, 0.125) reduces connectivity, which delays the upward transfer and increases the amount of the aqueous CO₂. However, specifically in the soil layer, the aqueous CO₂ accumulation reveals a non-monotonic trend that peaks at an intermediate ratio of 0.25. (3) CO₂ shows a cascading distribution across formations where reservoirs provide the primary storage, and the intermediate aquifer reduces the mass available for near-surface accumulation. This attenuation effect significantly reduces the CO₂ mass that reaches the soil layer, thereby controlling the strength and duration of near-surface environmental signals. This work offers a theoretical reference for formulating near-surface monitoring strategies for CO₂ leakage in GCS. 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

The lithology of multilayer superposed coal-measure reservoirs is highly interbedded, and the mechanical contrast between adjacent layers is significant, resulting in strong uncertainty in the initiation and propagation behavior of hydraulic fractures. To address the problem that the fracture-propagation mechanism under multi-lithology assemblages remains insufficiently understood, typical layered composite specimens were constructed, and large-scale true triaxial hydraulic fracturing physical simulation tests were performed to systematically investigate the effects of coal seam thickness, interlayer thickness, injection rate, and fracturing-fluid viscosity on fracturing pressure, fracture propagation path, and propagation capacity. The results show that when the coal seam thickness does not exceed 90 mm, cross-layer connectivity at the fracture breakthrough interface is more likely to occur. Interlayer thickness directly controls fracture-height growth. When the mudstone interlayer thickness is 40 mm, the fracture still retains the ability to propagate across layers, whereas this ability decreases significantly as the interlayer becomes thicker. When the injection rate is increased from 20 mL min⁻¹ to 30 mL min⁻¹, the overall pump-pressure platform rises, accompanied by a simultaneous increase in fracture extension scale and connectivity. As the fracturing-fluid viscosity increases from 3 mPa·s to 24 mPa·s, both the fracturing pressure and platform pressure increase significantly, and the fracture morphology gradually changes from dispersed propagation to more concentrated extension. The results further indicate that structural constraint factors (coal seam thickness and interlayer thickness) and dynamic driving factors (injection rate and fracturing-fluid viscosity) jointly control the spatial structure and pressure-response characteristics of fractures. Among these factors, interlayer thickness determines the conditions for cross-layer fracture propagation, injection rate and fluid viscosity control the ability to maintain net pressure within the fracture, and coal seam thickness constitutes an important geometric constraint. These findings provide an experimental basis for fracturing-parameter optimization and cross-layer stimulation design in multilayer superposed reservoirs. Full article

As reservoir development enters the middle and late stages, variations in formation pressure and water cut lead to significant changes in liquid supply capacity. Under conventional fixed stroke-per-minute (SPM) operation, the production capacity of beam pumping wells often fails to match the dynamically varying inflow, resulting in severe dynamic fluid level fluctuations and subsequent pump-off, gas locking, and abnormal rod string loading. To address these issues, this paper develops a dynamic fluid level model based on the operating mechanism of beam pumping wells, explicitly incorporating system uncertainties and reservoir disturbances. On this basis, a tube-based robust model predictive control (Tube-RMPC) strategy is proposed, in which nominal predictions are combined with local feedback compensation to effectively mitigate model uncertainties and external disturbances. Simulation results demonstrate that, compared with conventional PID control and traditional MPC methods, the proposed approach achieves superior performance in dynamic fluid level tracking accuracy, disturbance rejection, and closed-loop stability. Full article

Despite the success of artificial neural networks in solving numerous tasks, they face significant challenges, including difficulties in online adaptation and rapidly increasing energy consumption. As a biologically plausible alternative, spiking neural networks offer promising capabilities for efficient cognitive computing. Recently, a three-element spiking neuron model consisting of a threshold selector, a tunnel diode, and a capacitor was proposed. In this work, we experimentally validate this model using a threshold selector hardware emulator and demonstrate its dynamical equivalence to the biologically plausible Izhikevich neuron model. To evaluate the novel neuron’s applicability for cognitive computing, we implement a liquid state machine (LSM) reservoir architecture with spatially dependent random topology for synaptic weight distribution. Our simulations on the MNIST and Fashion-MNIST benchmarks demonstrate competitive classification accuracy (97.9% and 89.5%, respectively) while offering estimated energy efficiency and processing speed enhancements compared to existing FPGA-based and memristor-based spiking reservoir implementations. The developed reservoir is feasible for processing neuromorphic sensors output, including visual perception tasks. Full article