Impact of Osmotic Pressure on Seepage in Shale Oil Reservoirs DOI Open Access

Lijun Mu,

Xiaojia Xue,

Jie Bai

et al.

Fluid dynamics & materials processing, Journal Year: 2024, Volume and Issue: 20(6), P. 1365 - 1379

Published: Jan. 1, 2024

Following large-scale volume fracturing in shale oil reservoirs, well shut-in measures are generally employed.Laboratory tests and field trials have underscored the efficacy of fluid imbibition during phase augmenting productivity.Unlike conventional reservoirs exhibit characteristics such as low porosity, permeability, rich content organic matter clay minerals.Notably, osmotic pressure effects occurring between high-salinity formation water low-salinity fluids significant.The current understanding mobilization patterns crude micro-pores process remains nebulous, mechanisms underpinning not fully understood.This study introduces a theoretical approach, by which salt ion migration control equation is derived mathematical model for spontaneous introduced, able to account both capillary pressures.Results indicate that fluids, facilitate external into pores, thereby complementing forces displacing oil.When considering pressures, calculated depth increases 12% compared case where only present.The salinity difference reservoir significantly influences depth.Calculations shutin reveal matrix fractures reaches dynamic equilibrium after 28 days shut-in.During production phase, maximum seepage distance target block approximately 6.02 m.

Language: Английский

Integrated wellbore-surface pressure control production optimization for shale gas wells DOI Creative Commons

Xingyu Zhou,

Liming Zhang,

Ji Qi

et al.

Natural Gas Industry B, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
A new method for total organic carbon prediction of marine-continental transitional shale based on multivariate nonlinear regression DOI
X. Zhang, Yanjun Meng,

Taotao Yan

et al.

Frontiers of Earth Science, Journal Year: 2025, Volume and Issue: unknown

Published: May 22, 2025

Language: Английский

Citations

0
A Numerical Well Test Model for Fractured Wells in Fractured Shale Gas Reservoirs Considering Multiple Flow Mechanisms DOI
Zhiming Chen, Biao Zhou, Wei Yu

et al.

SPE Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: May 1, 2025

Summary The reservoirs of shale gas are typified by their low porosity and permeability, which generally necessitates the implementation large-scale hydraulic fracturing for economic development, thereby resulting in highly complex flow mechanisms. Well test analysis is an effective method reservoir parameter inversion, aids efficient development gas. However, there currently a lack accurate well model reservoirs. To address this issue, study proposes numerical fractured that incorporates various mechanisms, including adsorption/desorption, diffusion, stress sensitivity, fracture closure, non-Darcy flow. First, transient transmissibility correction factor introduced to account geometries early-time effects, enhancing accuracy. Subsequently, couples transport equations with geomechanical effects Langmuir isothermal adsorption, solved using finite volume combined Newton-Raphson iteration. subsequent step involves validation model’s This achieved comparing results those derived from standard software package. Sensitivity analyses further reveal natural fractures reduce pressure loss fluid supply, while adsorption delays decline at later stages; additionally, sensitivity closure intensify drop, particularly soft shales. Moreover, identifies eight stages, wellbore storage skin effect, bilinear flow, linear desorption, interfracture interference, transitional boundary-dominated Finally, field case Longmaxi Formation successfully matches production data, yielding inverted parameters such as matrix permeability conductivity. Overall, work provides powerful tool capable evaluating fractures, optimizing interpreting data.

Language: Английский

Citations

0
A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification DOI
Jianfei Bi, Jing Li, Keliu Wu

et al.

SPE Journal, Journal Year: 2023, Volume and Issue: 29(04), P. 2026 - 2043

Published: Dec. 13, 2023

Summary Surrogate models play a vital role in reducing computational complexity and time burden for reservoir simulations. However, traditional surrogate suffer from limitations autonomous temporal information learning restrictions generalization potential, which is due to lack of integration with physical knowledge. In response these challenges, physics-informed spatial-temporal neural network (PI-STNN) proposed this work, incorporates flow theory into the loss function uniquely integrates deep convolutional encoder-decoder (DCED) long short-term memory (ConvLSTM) network. To demonstrate robustness capabilities PI-STNN model, its performance was compared against both purely data-driven model same architecture renowned Fourier operator (FNO) comprehensive analysis. Besides, by adopting transfer strategy, trained adapted fractured fields investigate impact natural fractures on prediction accuracy. The results indicate that not only excels comparison but also demonstrates competitive edge over FNO simulation. Especially strongly heterogeneous fractures, can still maintain high Building accuracy, further offers distinct advantage efficiently performing uncertainty quantification, enabling rapid analysis investment decisions oil gas development.

Language: Английский

Citations

8
Blasingame production decline curve analysis for fractured tight sand gas wells based on embedded discrete fracture model DOI
Xianshan Liu, Shaoyang Geng, Peng Hu

et al.

Gas Science and Engineering, Journal Year: 2023, Volume and Issue: 121, P. 205195 - 205195

Published: Dec. 14, 2023

Language: Английский

Citations

6
Stratified yield prediction in fractured wells based on a multitasking bidirectional recurrent network DOI
Peng Chen, Liuting Zhou, Chunlei Jiang

et al.

Petroleum Science and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: July 1, 2024

Prediction of fluid production from hydraulically fractured wells is often difficult due to the incomplete understanding mechanism and limited availability data. In this paper, we propose a prediction model (MTL-Bi-LSTM-CNN) predict in all layers based on convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM) multi-task learning (MTL). The integrates fracturing parameters with data various each well. It harnesses both forward backward bi-directional information multiple layered capture intrinsic dependencies features, enhancing accuracy prediction. results demonstrate that stratified using proposed paper has higher smaller error than single-well unstratified Consequently, approach significantly improves accuracy, robustness, versatility predicting moisture content wells.

Language: Английский

Citations

2
Wellbore salt-deposition risk prediction of underground gas storage combining numerical modeling and machine learning methodology DOI
Zhiyue He, Yong Tang, Youwei He

et al.

Energy, Journal Year: 2024, Volume and Issue: 305, P. 132247 - 132247

Published: July 1, 2024

Language: Английский

Citations

2
Study on the Impact of Massive Refracturing on the Fracture Network in Tight Oil Reservoir Horizontal Wells DOI Open Access

Jianchao Shi,

Yanan Zhang,

Wantao Liu

et al.

Fluid dynamics & materials processing, Journal Year: 2024, Volume and Issue: 20(5), P. 1147 - 1163

Published: Jan. 1, 2024

Class III tight oil reservoirs have low porosity and permeability, which are often responsible for production rates limited recovery.Extensive repeated fracturing is a well-known technique to fix some of these issues.With such methods, existing fractures refractured, and/or new created facilitate communication with natural fractures.This study explored how different refracturing methods affect horizontal well fracture networks, special focus on morphology related fluid flow changes.In particular, the relied unconventional model (UFM).The evolution field after initial were analyzed accordingly.The simulation results indicated that increased formation energy reduced reservoir stress differences can promote expansion.It was shown length network, width complexity be improved, drainage area increased, distance gas seepage reduced, single significantly increased.

Language: Английский

Citations

1
Enhancing Shale Gas EUR Predictions with TPE optimized SMOGN: A Comparative Study of Machine Learning Algorithms in the Marcellus Shale with an Imbalanced Dataset DOI
Yildirim Kocoglu,

Sheldon Gorell,

Hossein Emadi

et al.

Gas Science and Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 205475 - 205475

Published: Oct. 1, 2024

Language: Английский

Citations

1
Predicting the productivity of fractured horizontal wells using few-shot learning DOI Creative Commons
Sen Wang, Ge Wen, Yulong Zhang

et al.

Petroleum Science, Journal Year: 2024, Volume and Issue: 22(2), P. 787 - 804

Published: Nov. 5, 2024

Language: Английский

Citations

1