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Recent Advances in Shale Oil and Gas Reservoirs
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Recent Advances in Shale Oil and Gas Reservoirs

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 3791

Special Issue Editors

Prof. Dr. Hongliang Wang

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Guest Editor
School of Energy Resources, China University of Geosciences, Beijing 100083, China
Interests: sequence stratigraphy; sedimentology; fine grained sedimentology; evaluation of shale oil and gas resources
Dr. Lin Wei

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Guest Editor
School of Energy Resources, China University of Geosciences, Beijing 100083, China
Interests: shale gas geochemistry; isotopic geochemistry; oil and gas geochemistry; hydrocarbon accumulation mechanism
Dr. Leifu Zhang

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Guest Editor
PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Interests: sedimentology of shales and carbonates; shale gas geochemistry; stochastic reservoir modeling

Special Issue Information

Dear Colleagues,

Revolution of shale oil and gas is changing the global energy supply map and international politics, which has attracted the attention of scholars around the world. The abundant shale oil and gas resources have become the primary chosen resource for countries to practice energy independence, which promotes the studies of shale oil and gas exploration theory and development technology. Within the current energy industry situation, the shale oil and gas industry has given birth to many research topics, including fine-grained sedimentology, unconventional reservoir geology, hydraulic fracturing technology, and shale resource evaluation. All these topics are closely related to the exploration and development of shale oil and gas resources. Currently, the single-well productions of shale oil and gas have the challenge of rapid decline during the producing stage. However, the continuous development of new exploration theories as well as new technologies is improving this situation and accelerating the process of shale oil and gas exploration and development. Within the most recent advances, research is focused on fine-grained sedimentology, the relationship between geological events and unconventional resources, pore-fracture systems of unconventional reservoirs, hydraulic fracturing technology, hydrocarbon occurrence, and the sweet-spotting evaluation of unconventional resources. This Special Issue aims to demonstrate the new exploration understanding and new development technologies related to shale oil and gas within this theme.

Topics welcome include but are not limited to:

  • Shale oil and gas resources;
  • Unconventional petroleum sedimentology;
  • Fine-grained sedimentology and shale reservoir evaluation;
  • Geological events and unconventional resources;
  • Source rock evaluation;
  • Pore-fracture systems of unconventional reservoirs;
  • Hydrocarbon occurrence and multi-phase flow in porous media;
  • Rock mechanics and hydraulic fracturing;
  • Sweet spot evaluation.

Prof. Dr. Hongliang Wang
Dr. Lin Wei
Dr. Leifu Zhang
Guest Editors

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. Energies is an international peer-reviewed open access semimonthly 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.

Published Papers (2 papers)

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Research

25 pages, 19854 KiB  
Article
Overpressure Generation and Evolution in Deep Longmaxi Formation Shale Reservoir in Southern Sichuan Basin: Influences on Pore Development
by Jia Yin, Lin Wei, Shasha Sun, Zhensheng Shi, Dazhong Dong and Zhiye Gao
Energies 2023, 16(6), 2533; https://doi.org/10.3390/en16062533 - 7 Mar 2023
Cited by 3 | Viewed by 1577
Abstract
Strong overpressure conditions are widely distributed in the deep Longmaxi Formation (Fm) shale reservoirs in the Southern Sichuan Basin, with pressure coefficients ranging from 1.75 to 2.45. Overpressure plays a positive role in the high yield of shale gas, but a detailed study [...] Read more.
Strong overpressure conditions are widely distributed in the deep Longmaxi Formation (Fm) shale reservoirs in the Southern Sichuan Basin, with pressure coefficients ranging from 1.75 to 2.45. Overpressure plays a positive role in the high yield of shale gas, but a detailed study of its generation mechanism, evolution history, and potential impact on pore development is still lacking. This study’s evidence from theoretical analysis and the logging response method indicates that hydrocarbon generation expansion is the main generation mechanism for strong overpressure. Through the combined analysis of basin modeling, inclusions analysis, and numerical simulation, pressure evolution at different stages is quantitatively characterized. The results show that, during the shale’s long-term subsidence process, the shale reservoir’s pressure coefficient increased to 1.40 because of oil generated by kerogen pyrolysis. Then it increased to 1.92 due to gas generated by residual oil cracking. During the late strong uplift process of the shale, temperature decrease, gas escape, and stratum denudation caused the pressure coefficient to first decrease to 1.84 and then increased to 2.04. Comparing pore characteristics under different pressure coefficients indicates that higher pressure coefficients within shale reservoirs contribute to the maintenance of total porosity and the development of organic macropores, but the influence on the morphology of organic pores is negligible. These results will provide the scientific basis for optimizing sweet spots and guiding shale gas exploration in the study area. Full article
(This article belongs to the Special Issue Recent Advances in Shale Oil and Gas Reservoirs)
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25 pages, 4761 KiB  
Article
Prediction of TOC in Lishui–Jiaojiang Sag Using Geochemical Analysis, Well Logs, and Machine Learning
by Xu Han, Dujie Hou, Xiong Cheng, Yan Li, Congkai Niu and Shuosi Chen
Energies 2022, 15(24), 9480; https://doi.org/10.3390/en15249480 - 14 Dec 2022
Cited by 1 | Viewed by 1665
Abstract
Total organic carbon (TOC) is important geochemical data for evaluating the hydrocarbon generation potential of source rocks. TOC is commonly measured experimentally using cutting and core samples. The coring process and experimentation are always expensive and time-consuming. In this study, we evaluated the [...] Read more.
Total organic carbon (TOC) is important geochemical data for evaluating the hydrocarbon generation potential of source rocks. TOC is commonly measured experimentally using cutting and core samples. The coring process and experimentation are always expensive and time-consuming. In this study, we evaluated the use of three machine learning (ML) models and two multiple regression models to predict TOC based on well logs. The well logs involved gamma rays (GR), deep resistivity (RT), density (DEN), acoustic waves (AC), and neutrons (CN). The ML models were developed based on random forest (RF), extreme learning machine (ELM), and back propagation neural network (BPNN). The source rock of Paleocene Yueguifeng Formation in Lishui–Jiaojiang Sag was taken as a case study. The number of TOC measurements used for training and testing were 50 and 27. All well logs and selected well logs (including AC, CN, and DEN) were used as inputs, respectively, for comparison. The performance of each model has been evaluated using different factors, including R2, MAE, MSE, and RMSE. The results suggest that using all well logs as input improved the TOC prediction accuracy, and the error was reduced by more than 30%. The accuracy comparison of ML and multiple regression models indicated the BPNN was the best, followed by RF and then multiple regression. The worst performance was observed in the ELM models. Considering the running time, the BPNN model has higher prediction accuracy but longer running time in small-sample regression prediction. The RF model can run faster while ensuring a certain prediction accuracy. This study confirmed the ability of ML models for estimating TOC using well logs data in the study area. Full article
(This article belongs to the Special Issue Recent Advances in Shale Oil and Gas Reservoirs)
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