Search Results (161)

The synergistic development of low-permeability reservoirs such as deep coalbed methane (CBM) and tight gas has emerged as a key technology to reduce development costs, enhance single-well productivity, and improve gas recovery. However, due to fundamental differences between coal seams and tight sandstones in their pore structure, permeability, water saturation, and pressure sensitivity, significant variations exist in their flow capacities and fluid production behaviors. To address the challenges of production allocation and main reservoir identification in the co-development of CBM and tight gas within deep gas-bearing basins, this study employs the transient multiphase flow simulation software OLGA to construct a representative dual-gas co-production well model. The regulatory mechanisms of the gas–liquid distribution, deliquification efficiency, and interlayer interference under two typical vertical stacking relationships—“coal over sand” and “sand over coal”—are systematically analyzed with respect to different tubing setting depths. A high-precision dynamic production allocation method is proposed, which couples the wellbore structure with real-time monitoring parameters. The results demonstrate that positioning the tubing near the bottom of both reservoirs significantly enhances the deliquification efficiency and bottomhole pressure differential, reduces the liquid holdup in the wellbore, and improves the synergistic productivity of the dual-reservoirs, achieving optimal drainage and production performance. Building upon this, a physically constrained model integrating real-time monitoring data—such as the gas and liquid production from tubing and casing, wellhead pressures, and other parameters—is established. Specifically, the model is built upon fundamental physical constraints, including mass conservation and the pressure equilibrium, to logically model the flow paths and phase distribution behaviors of the gas–liquid two-phase flow. This enables the accurate derivation of the respective contributions of each reservoir interval and dynamic production allocation without the need for downhole logging. Validation results show that the proposed method reliably reconstructs reservoir contribution rates under various operational conditions and wellbore configurations. Through a comparison of calculated and simulated results, the maximum relative error occurs during abrupt changes in the production capacity, approximately 6.37%, while for most time periods, the error remains within 1%, with an average error of 0.49% throughout the process. These results substantially improve the timeliness and accuracy of the reservoir identification. This study offers a novel approach for the co-optimization of complex multi-reservoir gas fields, enriching the theoretical framework of dual-gas co-production and providing technically adaptive solutions and engineering guidance for multilayer unconventional gas exploitation. Full article

This paper presents a numerical simulation study evaluating carbon dioxide capture and storage (CCS) feasibility for the Turceni Power Plant in Oltenia, Romania, using the nearby depleted Bibești-Bulbuceni gas reservoir. A comprehensive reservoir model was developed using Petrel software, integrating geological and reservoir engineering data for the formations of the Bibești-Bulbuceni structure, which is part of the western Moesian Platform. The static model incorporated realistic petrophysical inputs for the Meotian reservoirs. Dynamic simulations were performed using Eclipse compositional simulator with Peng–Robinson equation of state for a CH4-CO₂ system. The model was initialized with natural gas initially in place at 149 bar reservoir pressure, then produced through depletion to 20.85 bar final pressure, achieving 80% recovery factor. CO₂ injection simulations modeled a phased 19-well injection program over 25 years, with individual well constraints of 100 bar bottom-hole pressure and 200,000 Sm³/day injection rates. Results demonstrate successful injection of a 60 Mt CO₂, with final reservoir pressure reaching 101 bar. The modeling framework validates the technical feasibility of transforming Turceni’s power generation into a net-zero process through CCS implementation. Key limitations include simplified geochemical interactions and relying on historical data with associated uncertainties. This study provides quantitative evidence for CCS viability in depleted hydrocarbon reservoirs, supporting industrial decarbonization strategies. The strategy not only aligns with the EU’s climate-neutral policy but also enhances local energy security by repurposing existing geological resources. The findings highlight the potential of CCS to bridge the gap between current energy systems and a sustainable, climate-neutral future. Full article

Due to factors such as low porosity and permeability, thin sand body thickness, and strong interlayer heterogeneity, the fluid flow in the tight reservoir (beach-bar sandstone reservoir) exhibits obvious nonlinear seepage characteristics. Considering the time-varying physical parameters of different types of sand bodies, a nonlinear seepage coefficient is derived based on permeability and capillary force, and a low-velocity nonlinear seepage model for beach bar sand reservoirs is established. Based on core displacement experiments of different types of sand bodies, the low-velocity nonlinear seepage coefficient was fitted and numerical simulation of low-velocity nonlinear seepage in beach-bar sandstone reservoirs was carried out. The research results show that the displacement pressure and flow rate of low-permeability tight reservoirs exhibit a significant nonlinear relationship. The lower the permeability and the smaller the displacement pressure, the more significant the nonlinear seepage characteristics. Compared to the bar sand reservoir, the water injection pressure in the tight reservoir of the beach sand is higher. In the nonlinear seepage model, the bottom hole pressure of the water injection well increases by 10.56% compared to the linear model, indicating that water injection is more difficult in the beach sand reservoir. Compared to matrix type beach sand reservoirs, natural fractures can effectively reduce the impact of fluid nonlinear seepage characteristics on the injection and production process of beach sand reservoirs. Based on the nonlinear seepage characteristics, the beach-bar sandstone reservoir can be divided into four flow zones during the injection production process, including linear seepage zone, nonlinear seepage zone, non-flow zone affected by pressure, and non-flow zone not affected by pressure. The research results can effectively guide the development of beach-bar sandstone reservoirs, reduce the impact of low-speed nonlinear seepage, and enhance oil recovery. Full article

Horizontal wells have revolutionized hydrocarbon production by enhancing recovery efficiency and reducing environmental impact. This paper presents an enhanced Black Oil Model simulator, written in Visual Basic, for three-dimensional two-phase (oil and water) flow through porous media. Unlike most existing tools, this simulator is customized for horizontal well modeling and calibrated using extensive historical data from the South Rumaila Oilfield, Iraq. The simulator first achieves a strong match with historical pressure data (1954–2004) using vertical wells, with an average deviation of less than 5% from observed pressures, and is then applied to forecast the performance of hypothetical horizontal wells (2008–2011). The results validate the simulator’s reliability in estimating bottom-hole pressure (e.g., ±3% accuracy for HRU1 well) and water–oil ratios (e.g., WOR reduction of 15% when increasing horizontal well length from 1000 m to 2000 m). Notably, the simulator demonstrated that doubling the horizontal well length reduced WOR by 15% while increasing bottom-hole pressure by only 2%, highlighting the efficiency of longer wells in mitigating water encroachment. This work contributes to improved reservoir management by enabling efficient well placement strategies and optimizing extraction planning, thereby promoting both economic and resource-efficient hydrocarbon recovery. Full article

The deep and ultra-deep oil and gas resources often have the characteristics of high temperature and high pressure, with complex pressure systems and narrow safety density windows, so risks such as gas invasion and overflow are easy to occur during the drilling. In response to the problems of low management efficiency and large gas kick by traditional gas invasion treatment methods, this paper respectively established and compared three intelligent control models for bottom hole pressure (BHP) based on a PID controller, a fuzzy PID controller, and a fuzzy neural network PID controller based on the non-isothermal gas–liquid–solid three-phase transient flow heat transfer model in the annulus. The results show that compared with the PID controller and the fuzzy PID controller, the fuzzy neural network PID controller can adjust the control parameters adaptively and optimize the control rules in real-time; the efficiency of the fuzzy neural network PID controller to deal with a gas kick is improved by 45%, and the gas kick volume in the process of gas kick is reduced by 63.12%. The principal scientific novelty of this study lies in the integration of a fuzzy neural network PID controller with a non-isothermal three-phase flow model, enabling adaptive and robust bottom hole pressure regulation under complex gas invasion conditions, which is of great significance for reducing drilling risks and ensuring safe and efficient drilling. Full article

Proper determination of the bottomhole pressure in a gas lift well is essential to enhance production, tackle operating concerns, and use the least amount of gas. Mechanistic models, empirical correlation, and hybrid models are usually limited by the requirements for calibration, large amounts of inputs, or limited scope of work. Through this study, sixteen well-tested machine learning (ML) models, such as genetic programming-based symbolic regression and neural networks, are developed and studied to accurately predict flowing BHP at the perforation depth, using a dataset from 304 gas lift wells. The dataset covers a variety of parameters related to reservoirs, completions, and operations. After careful preprocessing and analysis of features, the models were prepared and tested with cross-validation, random sampling, and blind testing. Among all approaches, using the L-BFGS optimizer on the neural network gave the best predictions, with an R² of 0.97, low errors, and better accuracy than other ML methods. Upon using SHAP analysis, it was found that the injection point depth, tubing depth, and fluid flow rate are the main determining factors. Further using the model on 30 unseen additional wells confirmed its reliability and real-world utility. This study reveals that ML prediction for BHP is an effective alternative for traditional models and pressure gauges, as it is simpler, quicker, more accurate, and more economical. Full article

Deep coalbed methane (CBM) is rich in resources and is an important replacement resource for tight gas in China. Accurate prediction of post-fracture production and dynamic change characteristics of fractured wells of partial CBM is of great significance in predicting the final recovery rate. In terms of predicting time-series production, the problem one encounters is low prediction accuracy and poor generalisation ability under limited sample conditions. In this paper, we propose a hybrid deep neural network (AT-GRU-MTL) production prediction model based on the combination of an attention mechanism gated recurrent neural network (GRU) and multi-task learning (MTL), where the AT-GRU is responsible for capturing the nonlinear pattern of the production change, while introducing an MTL method that includes a cross-stitch network (CSN) and a weighted loss using homoskedasticity uncertainty to automatically determine the degree of sharing between multiple tasks and the weighting ratio of the total loss function. The model is applied to several typical deep CBM fracturing wells in China, and the accuracy of gas production prediction reaches 90%, while the accuracy of water production prediction is 68%. The experimental results show that, for the blocks with a very large difference in the order of magnitude of the gas and water production, it is very easy for a certain small order of magnitude to be suppressed from learning during the two-way multi-task learning process, which leads to deterioration of its prediction effect; at the same time, the adaptability of the model is evaluated, and it is found that the model is more advantageous for the wells that have been produced for approximately one year. Meanwhile, the evaluation of the model adaptability shows that the model is more dominant in the prediction of wells with production of about one and a half years. Based on the two test wells with shorter (380 days) and longer (709 days) spans, the results indicate that the model may have insufficient sensitivity to the sudden change of the ratio of gas to water and the failure of the dynamic generalisation of the matrix shrinkage–desorption coupling, and the introduction of physical constraints (such as bottomhole flow pressure, etc.) or the division of the data into the production stages may be attempted to deal with the case subsequently. The research results in this paper provide a theoretical basis for dynamic production prediction and analysis in oil and gas field sites. Full article

After hydraulic fracturing, hydraulic fractures and opened beddings are intertwined, which results in a complex fracture network in shale oil reservoirs. In addition, the migration of multi-phase fluids during fracturing and shut-in processes leads to complex flowback performance and brings difficulty to flowback strategies optimization. In this paper, taking the Daqing Gulong shale reservoir as an example, a numerical model, which considers oil–water–gas three-phase flow and the orthogonal fracture network, has been established for flowback period. The characteristics and influencing factors of flowback performance have been deeply studied, and the flowback modes of shale oil are reasonably optimized. Geological factors such as PTPG (pseudo-threshold pressure gradient), matrix permeability, and engineering factors such as opened bedding stress sensitivity, opened bedding permeability, and fracturing fluid distribution have obvious effects on the flowback performance, resulting in significant variations in production peaks, high production periods, and decline rates. Furthermore, three flowback modes distinguished by the BHP (bottom hole pressure) correspond to the three types of choke mode that have been optimized. This study reveals the main factors affecting the flowback performance. Meanwhile, the optimization method can be applied to optimize flowback strategies in Gulong and other similar shale reservoirs to obtain higher shale oil production. Full article

The development of deep coalbed methane (CBM) wells faces challenges such as significant reservoir depth, low permeability, and severe liquid loading in the wellbore. Traditional drainage and gas recovery techniques struggle to meet the dynamic production demands. This study, using the deep CBM wells at the Jishen 15A platform as an example, proposes a “cyclic gas lift–wellhead compression-vent gas recovery” composite synergy technology. By selecting a critical liquid-carrying model, innovating equipment design, and dynamically regulating pressure, this approach enables efficient production from low-pressure, low-permeability gas wells. This research conducts a comparative analysis of different critical liquid-carrying velocity models and selects the Belfroid model, modified for well inclination angle effects, as the primary model to guide the matching of tubing production and annular gas injection parameters. A mobile vent gas rapid recovery unit was developed, utilizing a three-stage/two stage pressurization dual-process switching technology to achieve sealed vent gas recovery while optimizing pipeline frictional losses. By combining cyclic gas lift with wellhead compression, a dynamic wellbore pressure equilibrium system was established. Field tests show that after 140 days of implementation, the platform’s daily gas production increased to 11.32 × 10⁴ m³, representing a 35.8% rise. The average bottom-hole flow pressure decreased by 38%, liquid accumulation was reduced by 72%, and cumulative gas production increased by 370 × 10⁴ m³. This technology effectively addresses gas–liquid imbalance and liquid loading issues in the middle and late stages of deep CBM well production, providing a technical solution for the efficient development of low-permeability CBM reservoirs. Full article

Hot water drilling is a drilling method that employs high-temperature and high-pressure hot water jetting to achieve ice melting drilling. Characterized by rapid drilling speed and large hole diameter, it is widely used for drilling observation holes in polar ice sheets and ice shelves. Understanding the hole-enlargement process at the bottom of hot water-drilled holes is crucial for rationally designing the structure of hot water drills. However, due to the complexity of heat transfer processes, no suitable theoretical model currently exists to accurately predict this process. To address this, this paper establishes an experimental platform for hot water drilling and conducts 24 sets of experiments under different drilling parameters using visualization techniques. The study reveals the influence mechanisms of drilling speed, hot water flow rate, hot water temperature, downhole drill shape, and nozzle structure on the hole-forming process at the borehole bottom. Experimental results indicate that the primary hole enlargement occurs near the nozzle, achieving 69–81% of the theoretical maximum borehole diameter. The thermal melting efficiency at the borehole bottom is approximately 80%, with about 20% of the input hot water energy heating the surrounding ice. Under identical hot water parameters, jet shapes and drill shapes exhibit minimal impact on borehole geometry. But the improvement of the jet speed and hot water temperature can accelerate the hole-forming process. Full article

Shale gas development has made significant contributions to the increase in natural gas production capacity in recent years, particularly in promoting the transformation of the energy structure and enhancing energy autonomy. However, with the deepening of shale gas field exploitation, particularly in the later stages of development, low-pressure gas wells and liquid accumulation issues have become increasingly apparent, posing significant challenges to the normal production of gas wells. Traditional single gas lifting and pressurization techniques have disadvantages such as high equipment investment, high operating costs, and inflexibility in switching, which make it difficult to meet the long-term and stable production needs of shale gas fields. Therefore, to overcome these challenges, this study proposes an innovative integrated pressurization–gas lifting multifunctional compressor process, which achieves the “pressurization ↔ gas lifting ↔ pressurization–gas lifting synergy” multi-mode intelligent switching function through modular integration design, resulting in higher production flexibility and efficiency. Adaptability assessments were completed on two typical shale gas platforms, and field test results show that the equipment can achieve stable production increases across all three functional modes. The pressurization mode demonstrates good adaptability in gas processing, efficiently pressurizing and transporting natural gas produced from the platform’s wells, meeting the increasing demand for gas export. The gas lifting function of the equipment can effectively address gas wells affected by wellbore or bottom-hole liquid accumulation, improving production conditions. In the synergy mode, the equipment design enables the effective collaboration of pressurization and gas lifting functions. Driven by the same power source, the two functional modules work efficiently together, adapting to complex production conditions where both gas lifting and pressurization for gas export occur simultaneously. The innovative process paradigm developed by this study provides an engineering solution for the entire lifecycle of shale gas field development, characterized by equipment integration and intelligent operation, offering significant economic benefits and promotional value. Full article

Ensuring the overall efficiency of hydraulic fracturing treatment depends on the ability to forecast bottomhole pressure. It has a direct impact on fracture geometry, production efficiency, and cost control. Since the complications present in contemporary operations have proven insufficient to overcome inherent uncertainty, the precision of bottomhole pressure predictions is of great importance. Achieving this objective is possible by employing machine learning algorithms that enable real-time forecasting of bottomhole pressure. The primary objective of this study is to produce sophisticated machine learning algorithms that can accurately predict bottomhole pressure while injecting guar cross-linked fluids into the fracture string. Using a large body of work, including 42 vertical wells, an extensive dataset was constructed and meticulously packed using processes such as feature selection and data manipulation. Eleven machine learning models were then developed using parameters typically available during hydraulic fracturing operations as input variables, including surface pressure, slurry flow rate, surface proppant concentration, tubing inside diameter, pressure gauge depth, gel load, proppant size, and specific gravity. These models were trained using actual bottomhole pressure data (measured) from deployed memory gauges. For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. The MSE and R² values of the best-performing machine learning predictors, primarily gradient boosting, decision trees, and neural network (L-BFGS) models, demonstrate a very low MSE value and high R² correlation coefficients when mapping the predictions of bottomhole pressure to actual downhole gauge measurements. R² values are reported as 0.931, 0.903, and 0.901, and MSE values are reported at 0.003, 0.004, and 0.004, respectively. Such low MSE values together with high R² values demonstrate the exceptionally high accuracy of the developed models. By illustrating how machine learning models for predicting pressure can act as a viable alternative to expensive downhole pressure gauges and the inaccuracy of conventional models and correlations, this work provides novel insight. Additionally, machine learning models excel over traditional models because they can accommodate a diverse set of cross-linked fracture fluid systems, proppant specifications, and tubing configurations that have previously been intractable within a single conventional correlation or model. Full article

Large-scale hydraulic fracturing is a prevalent technique for exploiting low-porosity and low-permeability shale reservoirs. The propagation and morphology of the hydraulic fracture in the laminated shale formations are significantly influenced by densely developed bedding planes, which can be classified into three categories: continuous, transitional, and discontinuous, with each characterized by distinct properties. This categorization complicates the prediction of the fracture propagation and the optimization of fracturing plans. In this research, a comparative study was proposed to describe fracture propagation and morphology in laminated shale with different types of bedding planes, employing the hydromechanically coupled discrete element method (DEM). The simulation results revealed that bedding planes of different types produce distinct impacts on the fracture propagation, leading to diverse fracture morphologies. In particular, it was found that the plane thickness affected the fracture propagation under low permeability, but the impact was insignificant under high permeability. Under different orientation angles, the continuous bedding planes showed distinct impacts on fracture propagation, while the transitional and discontinuous bedding planes consistently captured the hydraulic fracture. Moreover, the fluid viscosity and injection rate significantly influenced continuous and transitional bedding planes while having a minor effect on the discontinuous bedding planes. The optimal injection schemes incorporating varying injection rates or fluid viscosities were investigated. In addition, the impacts of small-scale bedding planes on fracture propagation were revealed. Furthermore, the bottom hole pressure variation and seismic event distribution were presented to provide complementary evidence of the fracture propagation. The simulation results can promote a comprehensive understanding of the fracture development in shale reservoirs. Full article

In China, most medium- and shallow-depth coalbed methane (CBM) reservoirs are in the middle to late stages of development. Exploiting CBM in unsaturated low-permeability reservoirs remains particularly challenging. This study investigates the evolution of reservoir pressure in rock strata during CBM extraction from a low-permeability coal seam in the Ordos Basin. By integrating the seepage equation, material balance equation, and fluid pressure theory, we establish a theoretical and numerical model of reservoir pressure dynamics under varying bottom-hole flowing pressures. The three-dimensional surface of reservoir pressure is characterized by the formation of a stable pressure drop funnel. The results show that gas–liquid flow capacity is significantly constrained in low-permeability reservoirs. A slower drainage control rate facilitates the formation of stable seepage channels and promotes the expansion of the seepage radius. Under ultra-low permeability (0.5 mD) to low permeability (2.5 mD) conditions, controlling the bottom-hole flowing pressure below the average value aids the effective expansion of the pressure drop funnel. Numerical simulations indicate that the seepage and desorption radii expand more effectively under low decline rates in low-permeability zones. Calculations based on production data reveal that, under ultra-low permeability conditions, Well V₁ exhibits a narrower and more elongated pressure drop funnel than Well V₂, which operates in a low permeability zone. Furthermore, well interference has a lesser effect on the expansion of the pressure drop funnel under ultra-low permeability conditions. These differences in the steady-state morphology of the pressure drop funnel ultimately lead to variations in production capacity. These findings provide a theoretical foundation and practical guidance for the rational development of low-permeability CBM reservoirs. Full article

Controlling oil well pressure during drilling is one of the most complex and hazardous processes in the exploration stage. The drilling system undergoes constant variations, influenced by factors such as drilling depth, which in turn affects other process parameters. Consequently, applying a time-invariant control strategy becomes impractical. This study aimed to identify the PID parameters necessary to regulate bottom-hole pressure during drilling across different operating depths, with the goal of maintaining system stability and robustness. To achieve this, the parameters were tested using a Gain Scheduling (GS) controller, which adjusted the control gains according to various operating points. In the first section, the development of a mathematical model of the process, based on fluid mechanics, is presented. Linearizing this model introduced an integrating element, which complicated the process dynamics. In the second section, we present the design of the controller using the Internal Model Control (IMC) tuning methodology to address the integration challenges. Finally, PID parameters for different drilling depths were obtained and integrated into the GS controller via Matlab Simulink. The controller’s performance was then evaluated through simulations of typical drilling issues, such as simulated disturbances, confirming its viability. The GS-controlled system was compared to a system using an adaptive controller, demonstrating superior performance in the former. Full article