[go: up one dir, main page]

 
 

Topic Editors

State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, Sichuan 610500, China
School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621010, China
Dr. Mingyang Wu
State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
Unconventional Petroleum Research Institute, China University of Petroleum Beijing, Beijing 102249, China

Exploitation and Underground Storage of Oil and Gas

Abstract submission deadline
30 June 2026
Manuscript submission deadline
30 September 2026
Viewed by
3897

Topic Information

Dear Colleagues,

As one of the most important fuels in modern society, oil and gas exploitation has been a research focus for a long period. Meanwhile, amidst the escalating global energy demand and accelerating energy transition, underground resource storage, including natural gas, carbon dioxide (CO2), hydrogen (H2), oil, etc., has attracted much attention in recent years. This Topic aims to converge the forefront scientific achievements in this research field and delve into novel theories, technologies, materials, processes, and equipment for oil and gas exploration, development, and underground storage. Sharing case studies and experiences in oil and gas development from deep-sea, deep-earth, and complex geological environments is encouraged, while also emphasizing environmental protection and carbon neutrality pathways throughout the development of storage processes . We eagerly anticipate your submissions and look forward to collectively contributing to the establishment of a safer, cleaner, and more efficient energy system. Topics of interest for publication include, but are not limited to, the following:

  • Carbon dioxide fracturing for enhanced permeability and carbon sequestration;
  • Multiphase fluid transport with phase change in fractured porous media;
  • Heat and mass transport in fractured porous media;
  • Mechanism of thermal fluid solidification in rock mass under the influence of fracture networks;
  • New advances in experimental modeling methods;
  • New advances in numerical modeling and simulation software;
  • Artificial intelligence and big data applications;
  • Coupled thermal–hydraulic–mechanical–chemical modeling and experiments;
  • Hydraulic fracturing and waterless fracturing in unconventional reservoirs.

Prof. Dr. Jianjun Liu
Prof. Dr. Rui Song
Dr. Liuke Huang
Dr. Yao Wang
Dr. Mingyang Wu
Dr. Gang Hui
Topic Editors

Keywords

  • multiphase flow
  • porous media
  • relative permeability
  • oil and gas exploitation
  • physical modeling
  • numerical simulation
  • geomechanics
  • petroleum engineering
  • energy storage
  • hydraulic fracturing
  • rock mechanics

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.3 2011 17.8 Days CHF 2400 Submit
Energies
energies
3.0 6.2 2008 17.5 Days CHF 2600 Submit
Journal of Marine Science and Engineering
jmse
2.7 4.4 2013 16.9 Days CHF 2600 Submit
Processes
processes
2.8 5.1 2013 14.4 Days CHF 2400 Submit
Resources
resources
3.6 7.2 2012 33.4 Days CHF 1600 Submit

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (7 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
16 pages, 6534 KiB  
Article
Experimental Study on Miscible Phase and Imbibition Displacement of Crude Oil Injected with CO2 in Shale Oil Reservoir
by Haibo He, Xinfang Ma, Bo Wang, Yuzhi Zhang, Jianye Mou and Jiarui Wu
Appl. Sci. 2024, 14(22), 10474; https://doi.org/10.3390/app142210474 - 14 Nov 2024
Viewed by 296
Abstract
Jimsar shale oil in China has undergone a rapid decline in formation energy and has a low recovery rate, with poor reservoir permeability. CO2 injection has become the main method for improving oil recovery. Pre-fracturing with CO2 energy storage in Jimsar [...] Read more.
Jimsar shale oil in China has undergone a rapid decline in formation energy and has a low recovery rate, with poor reservoir permeability. CO2 injection has become the main method for improving oil recovery. Pre-fracturing with CO2 energy storage in Jimsar shale oil has been performed, yielding a noticeable increase in oil recovery. However, the CO2 injection mechanism still requires a deeper understanding. Focusing on Jimsar shale oil in China, this paper studies the effect of CO2 on crude oil viscosity reduction, miscible phase testing, and the law of imbibition displacement. The results show that CO2 has a significant viscosity reduction effect on Jimsar shale oil, with a minimum miscible pressure between CO2 and Jimsar shale oil of 25.51 MPa, which can allow for miscibility under formation conditions. A rise in pressure increased the displacement capacity of supercritical CO2, as well as the displacement volume of crude oil. However, the rate of increase gradually declined. This research provides a theoretical basis for CO2 injection fracturing in Jimsar shale oil, which is helpful for improving the development effects of Jimsar shale oil. Full article
(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)
Show Figures

Figure 1

Figure 1
<p>Schematic diagram of aerated expansion experimental equipment.</p>
Full article ">Figure 2
<p>The relationship between the saturated pressure of crude oil and the amount of CO<sub>2</sub> injected.</p>
Full article ">Figure 3
<p>The relationship between the coefficient of volume expansion and pressure and the amount of CO<sub>2</sub> injection.</p>
Full article ">Figure 4
<p>The relationship between viscosity and CO<sub>2</sub> injection amount.</p>
Full article ">Figure 5
<p>The experimental device for determining the minimum miscible pressure.</p>
Full article ">Figure 6
<p>Schematic diagram of the minimum miscible pressure device.</p>
Full article ">Figure 7
<p>The minimum miscible pressure.</p>
Full article ">Figure 8
<p>Thermostat.</p>
Full article ">Figure 9
<p>High-temperature and high-pressure reaction tank.</p>
Full article ">Figure 10
<p>NMR equipment.</p>
Full article ">Figure 11
<p>The relationship between the nuclear magnetic semaphore and crude oil quality.</p>
Full article ">Figure 12
<p>The core after the replacement experiment.</p>
Full article ">Figure 13
<p>T2 spectra of the original state of seven cores.</p>
Full article ">Figure 14
<p>T2 spectra before and after displacement of 7 cores.</p>
Full article ">Figure 15
<p>Recovery rate after 24 h of displacement at different pressures.</p>
Full article ">Figure 16
<p>Recovery rate after different time of displacement at 20 MPa.</p>
Full article ">
19 pages, 6834 KiB  
Article
Experimental Study on the Efficiency of Fracturing Integrated with Flooding by Slickwater in Tight Sandstone Reservoirs
by Pingtian Fan, Yuetian Liu, Ziyu Lin, Haojing Guo and Ping Li
Processes 2024, 12(11), 2529; https://doi.org/10.3390/pr12112529 - 13 Nov 2024
Viewed by 374
Abstract
Tight reservoirs, with their nanoscale pore structures and limited permeability, present significant challenges for oil recovery. Composite fracturing fluids that combine both fracturing and oil recovery capabilities show great potential to address these challenges. This study investigates the performance of a slickwater-based fracturing [...] Read more.
Tight reservoirs, with their nanoscale pore structures and limited permeability, present significant challenges for oil recovery. Composite fracturing fluids that combine both fracturing and oil recovery capabilities show great potential to address these challenges. This study investigates the performance of a slickwater-based fracturing fluid, combined with a high-efficiency biological oil displacement agent (HE-BIO), which offers both production enhancement and environmental compatibility. Key experiments included tests on single-phase flow, core damage assessments, interfacial tension measurements, and oil recovery evaluations. The results showed that (1) the slickwater fracturing fluid effectively penetrates the rock matrix, enhancing oil recovery while minimizing environmental impact; (2) it causes substantially less damage to the reservoir compared to traditional guar gum fracturing fluid, especially in cores with little higher initial permeability; and that (3) oil recovery improves as HE-BIO concentration increases from 0.5% to 2.5%, with 2.0% as the optimal concentration for maximizing recovery rates. These findings provide a foundation for optimizing fracturing oil displacement fluids in tight sandstone reservoirs, highlighting the potential of the integrated fracturing fluid to enhance sustainable oil recovery. Full article
(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Natural cores from the tight reservoir of Chang 6 in the southwestern part of the Ordos Basin (Cores No. 1–16); (<b>b</b>) artificial cube cores (Cores No. 17–22).</p>
Full article ">Figure 2
<p>Flow chart of seepage characteristics experiment.</p>
Full article ">Figure 3
<p>Flow chart of core damage experiment: green arrows indicate fluid entering the matrix, and blue arrows indicate fluid exiting the matrix.</p>
Full article ">Figure 4
<p>This figure shows 0.1% JHFR-2 and 0.2% JHFD-2 with different concentrations of HE-BIO.</p>
Full article ">Figure 5
<p>Interface tension measuring device.</p>
Full article ">Figure 6
<p>Displacement experiment analysis system.</p>
Full article ">Figure 7
<p>Percolation characteristic curves of formation water.</p>
Full article ">Figure 8
<p>Percolation characteristic curves of the slickwater fracturing fluid (0.1% JHFR-2 + 0.2% JHFD-2).</p>
Full article ">Figure 9
<p>Percolation characteristic curves of the guar gum fracturing fluid.</p>
Full article ">Figure 10
<p>Relationship between the damage percentage of Cores No. 7–16 and initial permeability.</p>
Full article ">Figure 11
<p>Oil–water interface tension in the fracturing oil displacement integrated slickwater fracturing fluid.</p>
Full article ">Figure 12
<p>Oil–water interface tension changes in the fracturing oil displacement integrated slickwater fracturing fluid.</p>
Full article ">Figure 13
<p>Oil recovery rate with pore volume.</p>
Full article ">Figure 14
<p>The viscosity and oil recovery rate values increase compared to formation water with the increasing concentration of HE-BIO.</p>
Full article ">Figure 15
<p>(<b>a</b>) Inlet end of core after the application of slickwater fracturing fluid in single-phase percolation mechanism experiment (Core 4); (<b>b</b>) inlet end of core after application of the guar gum fracturing fluid in single-phase percolation mechanism experiment (Core 6); (<b>c</b>) percolation characteristic curves of Core 4 with the slickwater fracturing fluid and Core 6 with the guar gum fracturing fluid.</p>
Full article ">Figure 16
<p>The coordinated mechanism of HE-BIO and slickwater fracturing fluid.</p>
Full article ">
15 pages, 3856 KiB  
Article
A Prediction Method for Calculating Fracturing Initiation Pressure Considering the Modification of Rock Mechanical Parameters After CO2 Treatment
by Cuilong Kong, Yuxue Sun, Hao Bian, Jianguang Wei, Guo Li, Ying Yang, Chao Tang, Xu Wei, Ziyuan Cong and Anqi Shen
Processes 2024, 12(11), 2525; https://doi.org/10.3390/pr12112525 - 13 Nov 2024
Viewed by 309
Abstract
The establishment of a more realistic CO2 fracturing model serves to elucidate the intricate mechanisms underlying CO2 fracturing transformation. Additionally, it furnishes a foundational framework for devising comprehensive fracturing construction plans. However, current research has neglected to consider the influence of [...] Read more.
The establishment of a more realistic CO2 fracturing model serves to elucidate the intricate mechanisms underlying CO2 fracturing transformation. Additionally, it furnishes a foundational framework for devising comprehensive fracturing construction plans. However, current research has neglected to consider the influence of CO2 on rock properties during CO2 fracturing, resulting in an inability to precisely replicate the alterations in the reservoir post-CO2 injection into the formation. This disparity from the actual conditions poses a substantial limitation to the application and advancement of CO2 fracturing technology. This work integrates variations in the physical parameters of rocks after complete contact and reaction with CO2 into the numerical model of crack propagation. This comprehensive approach fully acknowledges the impact of pre-CO2 exposure on the mechanical parameters of reservoir rocks. Consequently, it authentically restores the reservoir state following CO2 injection, ensuring a more accurate representation of the post-fracturing conditions. In comparison with conventional numerical simulation methods, the approach outlined in this paper yields a reduction in the error associated with predicting fracturing pressure by 9.8%. Full article
(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)
Show Figures

Figure 1

Figure 1
<p>Experimental samples.</p>
Full article ">Figure 2
<p>Experimental flowchart.</p>
Full article ">Figure 3
<p>Triaxial compression experimental results.</p>
Full article ">Figure 4
<p>Experimental results of tensile strength test.</p>
Full article ">Figure 5
<p>Comparison of rocks’ elastic modulus before and after CO<sub>2</sub> treatment.</p>
Full article ">Figure 6
<p>Comparison of rocks’ Poisson’s ratio before and after CO<sub>2</sub> treatment.</p>
Full article ">Figure 7
<p>Comparison of rock tensile strength before and after CO<sub>2</sub> treatment.</p>
Full article ">Figure 8
<p>Comparison of rock permeability before and after CO<sub>2</sub> treatment.</p>
Full article ">Figure 9
<p>Actual fracturing curve.</p>
Full article ">Figure 10
<p>Numerical simulation results of pre-CO<sub>2</sub> fracturing.</p>
Full article ">Figure 11
<p>Calculation results of traditional methods for simulating pre-CO<sub>2</sub> fracturing.</p>
Full article ">
18 pages, 3400 KiB  
Article
Seepage–Diffusion Mechanism of Gas Kick Considering the Filtration Loss of Oil-Based Muds During Deepwater Drilling
by Yanli Guo, Weiqi Liu, Chaojie Song, Qingtao Gong and Yao Teng
J. Mar. Sci. Eng. 2024, 12(11), 2035; https://doi.org/10.3390/jmse12112035 - 10 Nov 2024
Viewed by 568
Abstract
As oil and gas exploration gradually advances into deep waters, the combined effects of various types of gas kick and the accurate calculation of the gas-kick volume have gained increasing attention. This study focused on gas kicks from permeable gas-bearing formations, considering the [...] Read more.
As oil and gas exploration gradually advances into deep waters, the combined effects of various types of gas kick and the accurate calculation of the gas-kick volume have gained increasing attention. This study focused on gas kicks from permeable gas-bearing formations, considering the mass transfer of gas in the filtration region of the drilling fluids and revealed the mechanisms of seepage-driven and diffusion-driven gas kicks. Based on seepage mechanics and diffusion theory, a comprehensive model for calculating gas-kick volume was established, considering the synergistic effect of gas-concentration-diffusion and negative-differential-pressure, as well as mass transfer in both the filtrate zone and the filter-cake zone. The new model showed high calculation accuracy. The sensitivity analysis showed that both the seepage-driven and diffusion-driven gas-kick volumes in the wellbore increased with increasing formation porosity and open-hole length, while the thickness of the filter cake had a strong inhibitory effect on both. Additionally, a “seepage–diffusion ratio” was introduced to reveal the gas-kick evolution pattern under a seepage–diffusion mechanism. Under specific case conditions, when the seepage–diffusion ratio was less than approximately 1%, diffusion-driven gas kick contributed more than seepage-driven gas kick; when the seepage–diffusion ratio exceeded 1%, seepage-driven gas kick contributed more than diffusion-driven gas kick. The research can provide crucial parameters for wellbore multiphase flow calculation and wellbore pressure prediction. Full article
(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)
Show Figures

Figure 1

Figure 1
<p>Physical model of seepage–diffusion-driven gas kick considering filtration loss. ① represents drilling fluid zone; ② represents outer filter cake; ③ represents inner filter cake; ④ represents filtrate zone; ⑤ represents unpolluted gas-bearing zone; ⑥ <span class="html-italic">dx</span> is a control unit along the wellbore axis for seepage-driven gas kick; ⑦ <span class="html-italic">dx</span> is a control unit along the wellbore axis for diffusion-driven gas kick.</p>
Full article ">Figure 2
<p>Distribution of cumulative volume of seepage-driven gas kick.</p>
Full article ">Figure 3
<p>Variation in cumulative volume of seepage-driven gas kick with formation porosity.</p>
Full article ">Figure 4
<p>Variation in cumulative volume of seepage-driven gas kick with open-hole length.</p>
Full article ">Figure 5
<p>Variation in cumulative volume of seepage-driven gas kick with filter-cake thickness.</p>
Full article ">Figure 6
<p>Variations in cumulative gas-kick volume and gas-kick rate with time. The red line represents the projection of data onto a horizontal plane, showing the variation in gas-kick volume over time. The green line represents the projection of data onto a vertical plane, indicating the changes in gas-kick rate over time.</p>
Full article ">Figure 7
<p>Effect of formation porosity on diffusion-driven gas kick. (<b>a</b>) Variation in cumulative gas-kick volume over time for different formation porosities. (<b>b</b>) Variation in 10-day cumulative gas-kick volume as function of porosity.</p>
Full article ">Figure 8
<p>Effect of open-hole length on diffusion gas kick. (<b>a</b>) Variation in cumulative gas-kick volume over time for different open-hole lengths. (<b>b</b>) Variation in 10-day cumulative gas-kick volume as function of open-hole length.</p>
Full article ">Figure 9
<p>Effect of drilling fluid filtration loss on diffusion-driven gas kick. (<b>a</b>) Variation in cumulative gas-kick volume over time for different filter-cake combinations. (<b>b</b>) Variation in 10-day cumulative gas-kick volume as function of filter-cake thickness.</p>
Full article ">Figure 10
<p>Evolution pattern of seepage–diffusion-driven gas kick. (<b>a</b>) Variation in cumulative gas-kick volume with seepage–diffusion ratio. (<b>b</b>) Variation in cumulative gas-kick volume with time when <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> <mo>%</mo> </mrow> </semantics></math>.</p>
Full article ">
17 pages, 4939 KiB  
Article
Experimental and Numerical Study on Nonlinear Shear Behavior and Constitutive Model of Deep Shale Laminae Planes
by Renyan Zhuo, Xinfang Ma, Jianmin Li, Shicheng Zhang and Junxiu Ma
Processes 2024, 12(11), 2445; https://doi.org/10.3390/pr12112445 - 5 Nov 2024
Viewed by 451
Abstract
The direct shear tests showed that the degradation of unevenness and waviness of the laminae plane is the primary reason for the dynamic decrease in shear strength. A shear constitutive model was proposed which considers the scale effect and the asperity geometry of [...] Read more.
The direct shear tests showed that the degradation of unevenness and waviness of the laminae plane is the primary reason for the dynamic decrease in shear strength. A shear constitutive model was proposed which considers the scale effect and the asperity geometry of the unevenness and waviness of the laminar plane. The evolution of the shear strength and stiffness with a normal stress and scale effect during the shearing of shale laminae planes was explored. The results show that high normal stress aggravates the stiffness hardening of laminae planes and forms larger peak shear stress and peak shear displacement. At the lab scale, the increase in the unevenness wavelength has a hardening effect on the shear stiffness and strength. The small-scale unevenness contributes most to the shear strength of shale laminae planes at the lab scale. At the field scale, the increase in the waviness wavelength has a softening effect on the shear stiffness and strength. Full article
(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)
Show Figures

Figure 1

Figure 1
<p>Schematic diagrams for explanation of the major difference between the Mohr–Coulomb shear strength and the dynamic shear strength. (<b>a</b>) Mohr–Coulomb shear strength; (<b>b</b>) dynamic shear strength.</p>
Full article ">Figure 2
<p>Sample preparation: (<b>a</b>) specimens of direct shear test of shale laminae planes; (<b>b</b>) shale laminae planes observed by polarizing microscope.</p>
Full article ">Figure 3
<p>Schematic diagram of the direct shear test device.</p>
Full article ">Figure 4
<p>Shear stress–shear displacement curves of shale laminae planes under different normal stress.</p>
Full article ">Figure 5
<p>Asperity degradation behavior of shale laminae planes under different normal stress: (<b>a</b>) 10 Mpa; (<b>b</b>) 15 Mpa; (<b>c</b>) 20 Mpa; (<b>d</b>) 30 Mpa.</p>
Full article ">Figure 6
<p>Five phases of shear stress–shear displacement curves.</p>
Full article ">Figure 7
<p>The wear model of lab-scale unevenness and waviness of laminae plane.</p>
Full article ">Figure 8
<p>Flowchart of the implementation of the constitutive model in 3DEC.</p>
Full article ">Figure 9
<p>Numerical model of direct shear test of shale laminae plane under constant normal stress. (<b>a</b>) Three-dimensional numerical model; (<b>b</b>) displacement constraint and initialization.</p>
Full article ">Figure 10
<p>Comparison between numerical and experimental results under different normal stresses.</p>
Full article ">Figure 11
<p>Comparison of direct shear curves of different joint constitutive models.</p>
Full article ">Figure 12
<p>Correlation between numerical and experimental results of the joint tested by Bandis.</p>
Full article ">Figure 13
<p>Shear stress–shear displacement curves under different normal stresses.</p>
Full article ">Figure 14
<p>Shear stress–shear displacement curves under different wavelength waviness at field-scale.</p>
Full article ">Figure 15
<p>Shear stress–shear displacement curves of different wavelength waviness at field scale.</p>
Full article ">
24 pages, 10284 KiB  
Article
Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks
by Shiping Tao, Yintong Guo, Haoyong Huang, Junfeng Li, Liqing Chen, Junchuan Gui and Guokai Zhao
Processes 2024, 12(10), 2259; https://doi.org/10.3390/pr12102259 - 16 Oct 2024
Viewed by 721
Abstract
Deep-learning-based (DL-based) seismic inversion has emerged as one of the state-of-the-art research areas in exploration geophysics with the development of artificial intelligence technology. Due to its good portability and high computational efficiency, this method has emerged as a data-driven approach for estimating subsurface [...] Read more.
Deep-learning-based (DL-based) seismic inversion has emerged as one of the state-of-the-art research areas in exploration geophysics with the development of artificial intelligence technology. Due to its good portability and high computational efficiency, this method has emerged as a data-driven approach for estimating subsurface properties. However, most of the current DL-based methods rely solely on seismic data, lacking the incorporation of prior information. In addition, these methods are usually performed trace-by-trace, resulting in insufficient horizontal constraints. These limitations make traditional methods less robust, particularly when dealing with high noise levels or limited data. To address these challenges, we propose a multi-input deep learning network for pre-stack inversion, which combines data-driven and model-driven approaches for optimization. The proposed method separately extracts features from the model and data, merging them to improve feature utilization. Moreover, by adopting a 2-D training unit, rather than a trace-by-trace approach, the method improves the horizontal continuity of the results. Tests on synthetic and real seismic data confirmed the robustness and improved stability of the proposed method, even under challenging conditions. This dual-driven approach significantly enhances the reliability of seismic inversion. Full article
(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)
Show Figures

Figure 1

Figure 1
<p>Schematic diagram of a CNN framework, including convolutional layers, pooling layers, and the final dense layers. The design of a CNN helps identify spatial patterns within seismic data, ultimately improving the accuracy of subsurface property predictions.</p>
Full article ">Figure 2
<p>Schematic diagram of the CNN network framework. It highlights the convolutional and pooling operations that identify spatial correlations in the input data, which is essential for accurate seismic property predictions.</p>
Full article ">Figure 3
<p>Seismic inversion based on the Unet framework, demonstrating the use of an encoder–decoder architecture to transform the seismic input into model parameters. The structure allows for a more effective translation of seismic data into subsurface properties, while preserving spatial relationships.</p>
Full article ">Figure 4
<p>The proposed multi-input neural network for pre-stack seismic inversion, featuring separate input streams for seismic data and prior geological models. The figure shows how features are extracted separately and then merged, enhancing the accuracy of the inversion results.</p>
Full article ">Figure 5
<p>The elastic parameters <math display="inline"><semantics> <msub> <mi>v</mi> <mi>p</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>v</mi> <mi>s</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> of the Marmousi-II model, providing a visual reference for the model’s complexity and how seismic inversion aims to resolve these subsurface properties.</p>
Full article ">Figure 6
<p>The inversion results for P-wave velocity <math display="inline"><semantics> <msub> <mi>v</mi> <mi>p</mi> </msub> </semantics></math> using (<b>a</b>) 1-D CNN, (<b>b</b>) 2-D CNN, (<b>c</b>) GICNN, (<b>d</b>) the proposed method with 1-D data–label pairs, (<b>e</b>) the proposed method with 2-D data–label pairs.</p>
Full article ">Figure 7
<p>The inversion results for S-wave velocity <math display="inline"><semantics> <msub> <mi>v</mi> <mi>s</mi> </msub> </semantics></math> using (<b>a</b>) 1-D CNN, (<b>b</b>) 2-D CNN, (<b>c</b>) GICNN, (<b>d</b>) the proposed method with 1-D data–label pairs, (<b>e</b>) the proposed method with 2-D data–label pairs.</p>
Full article ">Figure 8
<p>The inversion results for bulk density <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> using (<b>a</b>) 1-D CNN, (<b>b</b>) 2-D CNN, (<b>c</b>) GICNN, (<b>d</b>) the proposed method with 1-D data–label pairs, (<b>e</b>) the proposed method with 2-D data–label pairs.</p>
Full article ">Figure 9
<p>The inversion results for P-wave velocity <math display="inline"><semantics> <msub> <mi>v</mi> <mi>p</mi> </msub> </semantics></math> with 5 Hz (<b>c</b>,<b>e</b>) and 10 Hz (<b>d</b>,<b>f</b>) low-pass filtered initial models under a signal-to-noise ratio (SNR) of 3 (<b>a</b>,<b>c</b>,<b>e</b>) and 2 (<b>b</b>,<b>d</b>,<b>f</b>) using the conventional CNN method (<b>a</b>,<b>b</b>) and the proposed method (<b>c</b>–<b>f</b>).</p>
Full article ">Figure 10
<p>The extracted inversion result <math display="inline"><semantics> <msub> <mi>v</mi> <mi>p</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>v</mi> <mi>s</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> curves using the two methods with 10 Hz (<b>a</b>,<b>c</b>) and 5 Hz (<b>b</b>,<b>d</b>), initial models under SNRs of 3 (<b>a</b>,<b>b</b>) and 2 (<b>c</b>,<b>d</b>) from the <a href="#processes-12-02259-f009" class="html-fig">Figure 9</a>. The blue and green solid lines denote the ground-truth and the low-pass-filtered models, and the black and red dotted-dashed lines are the inverted results using the conventional CNN and the proposed methods.</p>
Full article ">Figure 11
<p>The elastic parameters <math display="inline"><semantics> <msub> <mi>v</mi> <mi>p</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>v</mi> <mi>s</mi> </msub> </semantics></math>, <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> of the overthrust model for transfer learning testing.</p>
Full article ">Figure 12
<p>The inverted results using transfer learning based on the proposed method, which highlights how the proposed method, with a small amount of real data, could yield accurate inversion results.</p>
Full article ">Figure 13
<p>The post-stack seismic profile of the real seismic data, and the black dashed lines indicating the location of wells A and B.</p>
Full article ">Figure 14
<p>The inverted elastic parameters <math display="inline"><semantics> <msub> <mi>v</mi> <mi>p</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>v</mi> <mi>s</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> using the conventional (<b>a</b>) CNN, (<b>b</b>) the GICNN, and (<b>c</b>) the proposed method. The figure demonstrates the superior stability, accuracy, and lateral continuity achieved by the proposed method.</p>
Full article ">
13 pages, 13631 KiB  
Article
Sensitivity Analysis of Depth-Controlled Oriented Perforation in Horizontal Wells Based on the 3D Lattice Method
by Haining Zhang, Yanhong Gou, Daojie Cheng, Fengsheng Zhang, Xunan Jia, Rui Gao and Yuwei Li
Processes 2024, 12(10), 2192; https://doi.org/10.3390/pr12102192 - 9 Oct 2024
Viewed by 596
Abstract
The main method used to exploit unconventional oil and gas reservoirs involves multi-cluster perforation combined with hydraulic fracturing in horizontal wells. However, as the use of this technology has expanded, challenges like reduced perforation efficiency and elevated fracture initiation pressure have surfaced. The [...] Read more.
The main method used to exploit unconventional oil and gas reservoirs involves multi-cluster perforation combined with hydraulic fracturing in horizontal wells. However, as the use of this technology has expanded, challenges like reduced perforation efficiency and elevated fracture initiation pressure have surfaced. The depth-controlled oriented perforation technique helps achieve uniform fracture initiation, enhance efficiency, and lower initiation pressure. In this study, a hydraulic fracturing fluid–solid coupling model at the perforation scale was established using the 3D lattice method to compare the near-wellbore fracture morphologies of depth-controlled oriented perforation, spiral perforation, and oriented perforation. Additionally, this study analyzes the effects of injection rate, reservoir elastic modulus, and horizontal stress difference on the fracture morphology and initiation pressure of depth-controlled oriented perforation. This study clarifies the applicability of depth-controlled oriented perforation in different types of reservoirs for the first time. The results indicate that intermediate fractures between spiral and oriented perforations are hindered, while depth-controlled oriented perforation ensures uniform fracture initiation. In the injection rate range of 0.144 to 0.360 L/min, an increase in injection rate accelerates the rise of fluid pressure within the perforations, leading to an increase in fracture initiation pressure. Therefore, excessively high injection rates are unfavorable for fracture initiation. Through depth-controlled oriented perforation, long and singular fractures can be formed in reservoirs with significant horizontal stress differences and high elastic moduli. Full article
(This article belongs to the Topic Exploitation and Underground Storage of Oil and Gas)
Show Figures

Figure 1

Figure 1
<p>Three-dimensional lattice model [<a href="#B25-processes-12-02192" class="html-bibr">25</a>].</p>
Full article ">Figure 2
<p>True triaxial hydraulic fracturing experiment model.</p>
Full article ">Figure 3
<p>Comparison of experimental fracture propagation trajectory with numerical simulation results.</p>
Full article ">Figure 4
<p>Numerical calculation results of injection pressure.</p>
Full article ">Figure 5
<p>Schematics of model layout (<b>a</b>) perspective view of the model, (<b>b</b>) spiral perforation, (<b>c</b>) oriented perforation, and (<b>d</b>) depth-controlled perforation.</p>
Full article ">Figure 6
<p>Distribution of depth-controlled oriented perforation.</p>
Full article ">Figure 7
<p>Frontal view of fracture morphology around the wellbore.</p>
Full article ">Figure 8
<p>Three-dimensional view of fracture morphology around the wellbore.</p>
Full article ">Figure 9
<p>Distribution of simplified depth-controlled oriented perforation.</p>
Full article ">Figure 10
<p>Frontal view of fracture morphology around the wellbore with different injection rates at 200 s.</p>
Full article ">Figure 11
<p>Three-dimensional view of fracture morphology around the wellbore with different injection rates at 200 s.</p>
Full article ">Figure 12
<p>Fluid pressure and Initiation pressure of different injection rates.</p>
Full article ">Figure 13
<p>Frontal view of fracture morphology around the wellbore with different elastic moduli at 200 s.</p>
Full article ">Figure 14
<p>Three-dimensional view of fracture morphology around the wellbore with different elastic moduli at 200 s.</p>
Full article ">Figure 15
<p>Fluid pressure and Initiation pressure of different elastic moduli.</p>
Full article ">Figure 16
<p>Frontal view of fracture morphology around the wellbore with different horizontal stress differences at 200 s.</p>
Full article ">Figure 17
<p>Three-dimensional view of fracture morphology around the wellbore with different horizontal stress differences at 200 s.</p>
Full article ">Figure 18
<p>Fluid pressure and initiation pressure of different horizontal stress differences.</p>
Full article ">
Back to TopTop