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Applied Sciences
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Petroleum Engineering: Production Forecasting, Process Design, and Implementation
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Petroleum Engineering: Production Forecasting, Process Design, and Implementation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 7304

Special Issue Editors

Dr. Wei Zhou

E-Mail Website
Guest Editor
School of Earth Science and Resources, Chang'an University, Xi'an, China
Interests: enhanced oil recovery; reservoir simulation; multiphase flow in porous media; chemical flooding; oilfield chemistry
Dr. Hongbin Yang

E-Mail Website
Guest Editor
College of Petroleum Engineering, China University of Petroleum (Huadong), Qingdao 266580, China
Interests: functional gelling control agent; low-cost and high-efficiency chemical flooding system; production fluid treatment; temperature-resistant cleaning fracturing fluid
Special Issues, Collections and Topics in MDPI journals
Dr. Martin Ma

E-Mail Website
Guest Editor
Computational Earth Science, Los Alamos National Laboratory, Los Alamos, NM, USA
Interests: reservoir simulation; production optmization; machine learning; CO2 storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect high-quality original research articles and review papers from the fields of Production Forecasting, Process Design, and Implementation in Petroleum Engineering. We encourage researchers from various fields within the journal’s scope to contribute original papers highlighting the latest developments in their research field or to invite relevant experts and colleagues to do so. Topics of interest for this Special Issue include, but are not limited to:

  • Production forecasting for various enhanced oil recovery (EOR) methods;
  • Design and field implementation of various EOR processes;
  • Machine learning applications in Petroleum Engineering;
  • Production forecasting for simulated horizontal wells with multi-stage hydraulic fractures;
  • Modeling and optimizing multi-stage hydraulic fractures;
  • Advanced optimization algorithms for production forecasting;
  • Experimental and numerical modeling of single and multiphase flows in oil and gas reservoirs;
  • Design and field implementation of water shutoff technologies and conformance control treatments;
  • Design and field implementation of CO2 utilization and storage;
  • Reservoir evaluation of unconventional oil and gas reservoirs;
  • Reservoir characterization of unconventional oil and gas reservoirs.

Dr. Wei Zhou
Dr. Hongbin Yang
Dr. Martin Ma
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. Applied Sciences 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 2400 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

  • production forecasting
  • field implementation
  • process design
  • optimization algorithms
  • machine learning
  • multiphase flows
  • CO2 utilization and storage
  • reservoir evaluation
  • reservoir characterization

Published Papers (6 papers)

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Research

16 pages, 6831 KiB  
Article
Optimization of CO2 Injection Huff and Puff Process in Shale Reservoirs Based on NMR Technology
by Yang Gao, Dehua Liu, Sichen Li, Liang Cheng and Jing Sun
Appl. Sci. 2024, 14(6), 2411; https://doi.org/10.3390/app14062411 - 13 Mar 2024
Viewed by 421
Abstract
The pore mobilization characteristics of CO2 when in shale reservoirs is an important indicator for evaluating the effectiveness of its application for enhanced recovery in shale reservoirs, and it is important to develop a comprehensive set of physical simulation methods that are [...] Read more.
The pore mobilization characteristics of CO2 when in shale reservoirs is an important indicator for evaluating the effectiveness of its application for enhanced recovery in shale reservoirs, and it is important to develop a comprehensive set of physical simulation methods that are consistent with actual field operations. This has underscored the need for efficient development techniques in the energy industry. The huff-n-puff seepage oil recovery method is crucial for developing tight oil reservoirs, including shale oil. However, the small pore size and low permeability of shale render conventional indoor experiments unsuitable for shale oil cores. Consequently, there is a need to establish a fully enclosed experimental method with a high detection accuracy to optimize the huff and puff process parameters. The NMR technique identifies oil and gas transport features in nanogaps, and in this study, we use low-field nuclear magnetic resonance (NMR) online displacement technology to conduct CO2 huff and puff experiments on shale oil, covering the gas injection, well stewing, and production stages. After conducting four rounds of huff-n-puff experiments, key process parameters were optimized, including the simmering time, huff-n-puff timing, number of huff-n-puff rounds, and the amount of percolant injected. The findings reveal that as the number of huff-n-puff rounds increases, the time required for well stabilization decreases correspondingly. However, the enhancement in recovery from additional huff-n-puff rounds becomes negligible after three rounds, showing only a 1.16% improvement. CO2 re-injection is required when the pressure falls to 70% of the initiaformation pressure to ensure efficient shale oil well development. This study also indicates that the most economically beneficial results are achieved when the injection volume of the huff-n-puff process is 0.44 pore volumes (PVs). Full article
(This article belongs to the Special Issue Petroleum Engineering: Production Forecasting, Process Design, and Implementation)
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16 pages, 3446 KiB  
Article
Calculation Model of Drainage Radius of Single-Layer/Multi-Layer Commingled Gas Production Wells in a Closed Constant-Volume Gas Reservoir and Its Application
by Cuiping Xin, Wei Zhou, Lei Zhang, Xiangyang Qiao, Yongke Wang and Yue Xiao
Appl. Sci. 2024, 14(5), 1873; https://doi.org/10.3390/app14051873 - 24 Feb 2024
Viewed by 550
Abstract
Gas drainage radius is the most important parameter for determining reasonable well spacing. However, the drainage radius of both single-layer and multi-layer commingled gas production wells cannot be directly obtained through reservoir well tests. In order to address the above challenge, for single-layer [...] Read more.
Gas drainage radius is the most important parameter for determining reasonable well spacing. However, the drainage radius of both single-layer and multi-layer commingled gas production wells cannot be directly obtained through reservoir well tests. In order to address the above challenge, for single-layer gas production wells in a closed constant-volume gas reservoir, a calculation model for drainage radius is derived using the modified flowing material balance method. The results indicate that the calculated error of this model is only 0.73% and much smaller than that of the flowing material balance method (5.78%), implying its high accuracy. For multi-layer commingled gas production wells, another calculation model of drainage radius is established by coupling the pseudo-steady-state production capacity equation with the material balance principle. The research results demonstrate that the novel calculation model has a maximum relative error of only 2.33% and requires only two production profile tests to rapidly calculate the drainage radius of each layer within the gas reservoir, suggesting its satisfactory simplicity, significant efficiency and high precision. The proposed calculation models of drainage radius achieve a convenient and rapid calculation for both single-layer and multi-layer commingled gas production wells, and fill the theoretical gap in efficient calculation of drainage radius for a closed constant-volume gas reservoir. Full article
(This article belongs to the Special Issue Petroleum Engineering: Production Forecasting, Process Design, and Implementation)
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13 pages, 1975 KiB  
Article
A New Model for Cleaning Small Cuttings in Extended-Reach Drilling Based on Dimensional Analysis
by Liping Jin, Gan Wang, Jingen Deng, Ze Li, Mingchi Zhu and Rong Wang
Appl. Sci. 2023, 13(22), 12118; https://doi.org/10.3390/app132212118 - 07 Nov 2023
Cited by 1 | Viewed by 665
Abstract
A wellbore’s cleanliness affects drilling efficiency, economy and drilling safety directly in extended-reach drilling operations. Wellbore cleaning in extended-reach drilling has always been a tough problem. Field experience has shown that inefficient transport of small cuttings is a main factor for excessive drag [...] Read more.
A wellbore’s cleanliness affects drilling efficiency, economy and drilling safety directly in extended-reach drilling operations. Wellbore cleaning in extended-reach drilling has always been a tough problem. Field experience has shown that inefficient transport of small cuttings is a main factor for excessive drag and torque during extended-reach drilling. However, very little is known about the transport of small cuttings. In this paper, we use the data fitting method of dimensional analysis and regression analysis to establish a wellbore cleaning model for high-inclination sections, then use water and Polyanionic Cellulose (PAC) as a drilling fluid to analyze the settlement cleaning of small cuttings in the wellbore. The data from the example wells were used for field simulations to finally derive many factors like drilling fluid density, drilling fluid displacement, drill pipe rotation speed, diameter of cuttings and annulus hydraulic diameter, which affect the thickness of the dimensionless cutting bed. The results show that the dimensionless cutting thickness decreases with increasing drilling fluid density, drilling fluid displacement and drill pipe rotation speed; and increases with increasing cutting size and annular hydraulics diameter. Meanwhile, the effect of drilling fluid discharge, drill pipe rotation speed and cutting size on the thickness of the dimensionless cutting bed decreases as their values increase. This model can be utilized to design parameters in drilling and to predict the transport of cuttings in high-inclination sections. The successful establishment of the wellbore cleaning model for small cuttings in high-inclination sections of extended-reach drilling wells is highly innovative, which is a successful combination of theory and practice and has important guiding significance for promoting the development of wellbore cleaning technology. Full article
(This article belongs to the Special Issue Petroleum Engineering: Production Forecasting, Process Design, and Implementation)
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14 pages, 6965 KiB  
Article
Enhancing Oil Recovery and Altering Wettability in Carbonate Reservoir Rocks through (3-Glycidoxypropyl)trimethoxysilane–SiO2 Nanofluid Injection
by Hochang Jang and Jeonghwan Lee
Appl. Sci. 2023, 13(19), 11105; https://doi.org/10.3390/app131911105 - 09 Oct 2023
Viewed by 710
Abstract
This study analyzes the impact of injection condition design factors of (3-glycidoxypropyl)trimethoxysilane (GPTMS)–SiO2 nanofluid on improving wettability and oil recovery through flotation and core flooding tests, respectively. Flotation tests were conducted to assess improvements in wettability that resulted from varying nanoparticle concentration, [...] Read more.
This study analyzes the impact of injection condition design factors of (3-glycidoxypropyl)trimethoxysilane (GPTMS)–SiO2 nanofluid on improving wettability and oil recovery through flotation and core flooding tests, respectively. Flotation tests were conducted to assess improvements in wettability that resulted from varying nanoparticle concentration, reaction time, and treatment temperature. The test results demonstrated that the hydrophilic sample ratio increased by up to 97.75% based on the nanoparticle reaction, confirming significant wettability improvement in all samples. Additionally, time-dependent fluid-flow experiments were conducted to validate oil recovery and rock–fluid interactions. In these experiments, for a 24-h reaction time, nanofluid injection caused a decrease in the maximum contact angle (43.4° from 166.5°) and a remarkable enhancement in the oil recovery rate by over 25%. Moreover, variations in contact angle and sample permeability were observed as the reaction time increased. Subsequently, the core flooding test revealed a critical reaction time of 24 h, maximizing oil recovery while minimizing permeability. Below this point in time, wettability improvement did not significantly enhance oil recovery. Conversely, beyond this threshold, additional adsorption due to particle aggregation decreased permeability, causing reduced oil recovery. Therefore, GPTMS–SiO2 nanofluid can be utilized as an injection fluid to enhance oil recovery in high-temperature and high-salinity carbonate reservoirs. Full article
(This article belongs to the Special Issue Petroleum Engineering: Production Forecasting, Process Design, and Implementation)
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17 pages, 5788 KiB  
Article
Enhancing Production Prediction in Shale Gas Reservoirs Using a Hybrid Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP) Model
by Xianlin Ma, Mengyao Hou, Jie Zhan and Rong Zhong
Appl. Sci. 2023, 13(17), 9827; https://doi.org/10.3390/app13179827 - 30 Aug 2023
Cited by 2 | Viewed by 893
Abstract
Shale gas has revolutionized the global energy supply, underscoring the importance of robust production forecasting for the effective management of well operations and gas field development. Nonetheless, the intricate and nonlinear relationship between gas production dynamics and physical constraints like shale formation properties [...] Read more.
Shale gas has revolutionized the global energy supply, underscoring the importance of robust production forecasting for the effective management of well operations and gas field development. Nonetheless, the intricate and nonlinear relationship between gas production dynamics and physical constraints like shale formation properties and engineering parameters poses significant challenges. This investigation introduces a hybrid neural network model, GRU-MLP, to proficiently predict shale gas production. The GRU-MLP architecture can capture sequential dependencies within production data as well as the intricate nonlinear correlations between production and the governing constraints. The proposed model was evaluated employing production data extracted from two adjacent horizontal wells situated within the Marcellus Shale. The comparative analysis highlights the superior performance of the GRU-MLP model over the LSTM and GRU models in both short-term and long-term forecasting. Specifically, the GRU model’s mean absolute percentage error of 4.7% and root mean squared error of 120.03 are notably 66% and 80% larger than the GRU-MLP model’s performance in short-term forecasting. The accuracy and reliability of the GRU-MLP model make it a promising tool for shale gas production forecasting. By providing dependable production forecasts, the GRU-MLP model serves to enhance decision-making and optimize well operations. Full article
(This article belongs to the Special Issue Petroleum Engineering: Production Forecasting, Process Design, and Implementation)
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20 pages, 4738 KiB  
Article
Hybrid Model of Machine Learning Method and Empirical Method for Rate of Penetration Prediction Based on Data Similarity
by Fei Zhou, Honghai Fan, Yuhan Liu, Hongbao Zhang and Rongyi Ji
Appl. Sci. 2023, 13(10), 5870; https://doi.org/10.3390/app13105870 - 10 May 2023
Cited by 2 | Viewed by 3005
Abstract
The rate of penetration (ROP) is an important indicator affecting the drilling cost and drilling performance. Accurate prediction of the ROP has important guiding significance for increasing the drilling speed and reducing costs. Recently, numerous studies have shown that machine learning techniques are [...] Read more.
The rate of penetration (ROP) is an important indicator affecting the drilling cost and drilling performance. Accurate prediction of the ROP has important guiding significance for increasing the drilling speed and reducing costs. Recently, numerous studies have shown that machine learning techniques are an effective means to accurately predict the ROP. However, in petroleum engineering applications, its robustness and generalization cannot be guaranteed. The traditional empirical model has good robustness and generalization ability. Based on the quantification of data similarity, this paper establishes a hybrid model combining a machine learning method and an empirical method, which combines the high prediction accuracy of the machine learning method with the good robustness and generalization of the empirical method, overcoming the shortcomings of any single model. The AE-ED (the Euclidean Distance between the input data and reconstructed data from the autoencoder model) is defined to measure the data similarity, and according to the data similarity of each new piece of input data, the hybrid model chooses the corresponding single model to calculate. The results show that the hybrid model is better than any single model, and all the evaluation indicators perform better, making it more suitable for the ROP prediction in this field. Full article
(This article belongs to the Special Issue Petroleum Engineering: Production Forecasting, Process Design, and Implementation)
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