Advanced Reservoir Simulation and Modelling, Thermal and Enhanced Oil Recovery Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 1 November 2024 | Viewed by 1732

Special Issue Editor


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Guest Editor
Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Interests: enhanced oil recovery; new material; drilling

Special Issue Information

Dear Colleagues,

The topic “Advanced Reservoir Simulation and Modelling, Thermal and Enhanced Oil Recovery Processes” covers a set of equations, assumptions, descriptions, fluid dynamics, and active processes in the reservoir using computer models. For this Special Issue, a collection of original articles and review articles covering the topic are invited. Advanced studies help us understand the nature of the analyzed method and its potential application in the real world. The topic of this Special Issue covers a large range of research, and we ask you to publish articles showing the proficiency, efficiency, and effectiveness of numerical methods to address scientific and engineering problems. We also invite articles highlighting in-depth calculations of reservoirs using advanced simulation software. The calculations include reservoir porosity, permeability, and reservoir engineering using computer models to identify flow fluids through porous media.

Dr. Syed Muhammad Shakil Hussain
Guest Editor

Manuscript Submission Information

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Keywords

  • reservoir engineering
  • computer models
  • fluid flow
  • porosity
  • permeability
  • fluid dynamics
  • thermal behavior
  • enhanced oil recovery
  • reservoir management
  • real-time data

Published Papers (2 papers)

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Research

13 pages, 844 KiB  
Article
Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data
by Minjing Chen, Zhan Qu, Wei Liu, Shanjie Tang, Zhengkai Shang, Yanfei Ren and Jinliang Han
Processes 2024, 12(5), 932; https://doi.org/10.3390/pr12050932 - 3 May 2024
Viewed by 471
Abstract
The accurate prediction of gas well production is one of the key factors affecting the economical and efficient development of tight gas wells. The traditional oil and gas well production prediction method assumes strict conditions and has a low prediction accuracy in actual [...] Read more.
The accurate prediction of gas well production is one of the key factors affecting the economical and efficient development of tight gas wells. The traditional oil and gas well production prediction method assumes strict conditions and has a low prediction accuracy in actual field applications. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some limitations. Only learning from data leads to the poor generalization ability and anti-interference ability of prediction models. To solve this problem, a production prediction method of tight gas wells based on the decline curve and data-driven neural network is established in this paper. Based on the actual production data of fractured horizontal wells in three tight gas reservoirs in the Ordos Basin, the prediction effect of the Arps decline curve model, the SPED decline curve model, the MFF decline curve model, and the combination of the decline curve and data-driven neural network model is compared and analyzed. The results of the case analysis show that the MFF model and the combined data-driven model have the highest accuracy, the average absolute percentage error is 14.11%, and the root-mean-square error is 1.491, which provides a new method for the production prediction of tight gas wells in the Ordos Basin. Full article
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20 pages, 7834 KiB  
Article
A Comprehensive Investigation of the Relationship between Fractures and Oil Production in a Giant Fractured Carbonate Field
by Riyaz Kharrat, Ali Kadkhodaie, Siroos Azizmohammadi, David Misch, Jamshid Moghadasi, Hashem Fardin, Ghasem Saedi, Esmaeil Rokni and Holger Ott
Processes 2024, 12(4), 631; https://doi.org/10.3390/pr12040631 - 22 Mar 2024
Viewed by 950
Abstract
This study examines the connections between various fracture indicators and production data with an example from one of the giant fields in the Middle East producing complex fractured carbonate lithologies. The field under study hosts two reservoirs with a long development and production [...] Read more.
This study examines the connections between various fracture indicators and production data with an example from one of the giant fields in the Middle East producing complex fractured carbonate lithologies. The field under study hosts two reservoirs with a long development and production history, including carbonates from the Asmari and Bangestan Formations. A fracture intensity map was generated based on the interpretation of image logs from 28 wells drilled within the field. Mud loss data were collected and mapped based on the geostatistical Gaussian Random Function Simulation (GRFS) algorithm. Maximum curvature maps were generated based on Asmari structural surface maps. Comparing the results shows a good agreement between the curvature map, fault distribution model, mud loss map, fracture intensity map, and productivity index. The results of image log interpretations led to the identification of four classes of open fractures, including major open fractures, medium open fractures, minor open fractures, and hairline fractures. Using the azimuth and dip data of the four fracture sets mentioned above, the fracture intensity log was generated as a continuous log for each well with available image log data. For this purpose, the fracture intensity log and a continuous fracture network (CFN) model were generated. The continuous fracture network model was used to generate a 3D discrete fracture network (DFN) for the Asmari Formation. Finally, a 3D upscaled model of fracture dip and azimuth, fracture porosity, fracture permeability, fracture length, fracture aperture, and the sigma parameter (the connectivity index between matrix and fracture) were obtained. The results of this study can illuminate the modeling of intricate reservoirs and the associated production challenges, providing insights not only during the initial production phase but also in the application of advanced oil recovery methods, such as thermal recovery. Full article
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