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JMSE
Special Issues
Production Prediction in Onshore and Offshore Tight Reservoirs
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Production Prediction in Onshore and Offshore Tight Reservoirs

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Geological Oceanography".

Deadline for manuscript submissions: 5 August 2024 | Viewed by 1225

Special Issue Editor

Prof. Dr. Gang Lei

E-Mail Website
Guest Editor
Faculty of Engineering, China University of Geosciences, Wuhan, China
Interests: fractured tight reservoir; stress-dependent permeability; fracture penetration extent; theoretical model

Special Issue Information

Dear Colleagues,

In the last decade, with the rapid development and successful application of theoretical models, experimental techniques, and machine learning (ML) techniques in Petroleum Engineering, incredible progresses have been achieved in the production prediction of producers (fractured wells, multi-branch wells) in onshore and offshore tight reservoirs. This Special Issue intends to publish the latest progresses and achievements in research regarding the production prediction in onshore and offshore tight reservoirs through the use of methods, and their combinations, based on experimental techniques, theoretical models, and ML techniques. We invite papers concerning topics including, but not limited to, the following:

  • Reservoir evaluation and characterization of onshore and offshore tight reservoirs
  • Production prediction for various enhanced oil recovery (EOR) methods applied in onshore and offshore tight reservoirs
  • Experimental and numerical modeling of single and multiphase flows in onshore and offshore tight reservoirs
  • Advanced optimization algorithms for production prediction in onshore and offshore tight reservoirs
  • Machine learning and data science applied for the production prediction in onshore and offshore tight reservoirs

Prof. Dr. Gang Lei
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • onshore tight reservoirs
  • offshore tight reservoirs
  • enhanced oil recovery (EOR)
  • machine learning
  • data science

Published Papers (2 papers)

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Research

26 pages, 27938 KiB  
Article
Pore Pressure Prediction for High-Pressure Tight Sandstone in the Huizhou Sag, Pearl River Mouth Basin, China: A Machine Learning-Based Approach
by Jin Feng, Qinghui Wang, Min Li, Xiaoyan Li, Kaijin Zhou, Xin Tian, Jiancheng Niu, Zhiling Yang, Qingyu Zhang and Mengdi Sun
J. Mar. Sci. Eng. 2024, 12(5), 703; https://doi.org/10.3390/jmse12050703 - 24 Apr 2024
Viewed by 428
Abstract
A growing number of large data sets have created challenges for the oil and gas industry in predicting reservoir parameters and assessing well productivity through efficient and cost-effective techniques. The design of drilling plans for a high-pressure tight-sand reservoir requires accurate estimations of [...] Read more.
A growing number of large data sets have created challenges for the oil and gas industry in predicting reservoir parameters and assessing well productivity through efficient and cost-effective techniques. The design of drilling plans for a high-pressure tight-sand reservoir requires accurate estimations of pore pressure (Pp) and reservoir parameters. The objective of this study is to predict and compare the Pp of Huizhou Sag, Pearl River Mouth Basin, China, using conventional techniques and machine learning (ML) algorithms. We investigated the characteristics of low-permeability reservoirs by observing well-logging data sets and cores and examining thin sections under a microscope. In the reservoir zone, the average hydrocarbon saturation is 55%, and the average effective porosity is 11%. The tight sandstone reservoirs consist of fine- to extremely fine-grained argillaceous feldspathic sandstone. The mean absolute error for reservoir property prediction is 1.3%, 2.2%, and 4.8%, respectively, for effective porosity, shale volume, and water saturation. Moreover, the ML algorithm was employed to cross-check the validity of the prediction of Pp. Combining conventional and ML techniques with the core data demonstrates a correlation coefficient (R2) of 0.9587, indicating that ML techniques are the most effective in testing well data. This study shows that ML can effectively predict Pp at subsequent depths in adjacent geologically similar locations. Compared to conventional methods, a substantial data set and ML algorithms improve the precision of Pp predictions. Full article
(This article belongs to the Special Issue Production Prediction in Onshore and Offshore Tight Reservoirs)
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17 pages, 10728 KiB  
Article
Pressure Analysis of Onshore and Offshore Shale Gas Reservoirs under Constant-Rate Condition Considering Thin Sandstone Layer and Interlayer Cross-Flow
by Shiming Wei and Kaixuan Qiu
J. Mar. Sci. Eng. 2024, 12(3), 457; https://doi.org/10.3390/jmse12030457 - 06 Mar 2024
Viewed by 547
Abstract
The extraction of shale gas from onshore and offshore shale gas reservoirs will play an important role in meeting China’s future energy needs, which will not only help alleviate the energy crisis but also contribute to climate change mitigation. As for the target [...] Read more.
The extraction of shale gas from onshore and offshore shale gas reservoirs will play an important role in meeting China’s future energy needs, which will not only help alleviate the energy crisis but also contribute to climate change mitigation. As for the target shale formation enriched by thin sandstone layers in typical basins, an analytical calculation method is proposed to perform pressure analysis for multi-layer shale gas reservoirs considering the adsorption–desorption characteristics of shale layer and the interlayer cross-flow. Firstly, the changes in storage capacity and flow resistance are obtained by using the distance of investigation equation. According to the electrical analogy, the equivalent total storage capacity and flow resistance can be calculated considering the sandstone-shale crossflow. Because production from one time step to the other causes depletion of the storage capacity, the reservoir pressure in different time steps can be calculated based on the material balance equation. Numerical models have been constructed based on three typical reservoir lithology combinations (sandstone-shale, shale-sandstone-shale and sandstone-shale-sandstone) to validate the accuracy of the proposed analytical calculation method. Furthermore, three important factors (porosity, the ratio of horizontal/vertical permeability (kh/kv) and the layer thickness) have been selected for the sensitivity analysis to verify the stability. The comparative results indicate that the proposed analytical calculation method is suitable for pressure analysis in shale gas reservoirs containing thin sandstone layers. It will provide theoretical support for the further enhancement of the production of this type of gas reservoirs. Full article
(This article belongs to the Special Issue Production Prediction in Onshore and Offshore Tight Reservoirs)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Planned Paper 1
Title: A machine learning method for predicting productivity of silty and sand hydrate reservoirs.
 
Planned Paper 2
Title: A thermo-hydro-mechanical model for predicting relative permeability of gas hydrate sediments.
 
Planned Paper 3
Title: Gas and sand production of gas hydrate sediments: A experimental and theoretical work.
 
Planned Paper 4
Title: A upscaling model for modeling multi-phase flow in tight porous media.
 
Planned Paper 5
Title: Research on non-linear flow in gas hydrate sediments: An analytical model
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