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15 pages, 1154 KiB

Open AccessArticle

Surfactant–Polymer Flooding: Chemical Formula Design and Evaluation for High-Temperature and High-Salinity Qinghai Gasi Reservoir

by Jinlong Sun, Yifeng Liu, Xiuyu Zhu, Futang Hu, Yuanyuan Wang, Xiaoling Yi, Zhuoyan Zhu, Weidong Liu, Youyi Zhu and Qingfeng Hou

Processes 2024, 12(6), 1082; https://doi.org/10.3390/pr12061082 - 24 May 2024

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Abstract

The Gasi reservoir in the Qinghai oilfield is a typical high-temperature and high-salinity reservoir, with an average temperature and average salinity of 70.0 °C and 152,144 mg/L, respectively. For over 30 years since 1990, water flooding has been the primary method for enhancing [...] Read more.

The Gasi reservoir in the Qinghai oilfield is a typical high-temperature and high-salinity reservoir, with an average temperature and average salinity of 70.0 °C and 152,144 mg/L, respectively. For over 30 years since 1990, water flooding has been the primary method for enhancing oil recovery. Recently, the Gasi reservoir has turned into a mature oilfield. It possesses a high water cut of 76% and a high total recovery rate of 47%. However, the main developing enhanced oil recovery (EOR) technology for the development of the Gasi reservoir in the next stage is yet to be determined. Surfactant–polymer (SP) flooding, which can reduce the oil–water interfacial tension and increase the viscosity of the water phase, has been widely applied to low-temperature and low-salinity reservoirs across China in the past few decades, but it has rarely been applied to high-temperature and high-salinity reservoirs such as the Gasi reservoir. In this study, the feasibility of SP flooding for high-temperature and high-salinity reservoirs was established. Thanks to the novel surfactant and polymer products, an SP flooding formula with surfactants ZC-2/B2 and polymer BRH-325 was proposed for Gasi. The formula showed a low interfacial tension of 10⁻² mN/m and a high viscosity of 18 mPa·s in simulated reservoir conditions. The oil displacement experiment demonstrated that this formula can enhance the oil recovery rate by 26.95% upon water flooding at 64.64%. This study provides a feasible EOR candidate technology for high-temperature and high-salinity reservoirs, as exemplified by the Qinghai Gasi reservoir. Full article

(This article belongs to the Special Issue Advanced Reservoir Simulation and Modelling, Thermal and Enhanced Oil Recovery Processes)

13 pages, 844 KiB

Open AccessArticle

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

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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

(This article belongs to the Special Issue Advanced Reservoir Simulation and Modelling, Thermal and Enhanced Oil Recovery Processes)

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20 pages, 7834 KiB

Open AccessArticle

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 999

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

(This article belongs to the Special Issue Advanced Reservoir Simulation and Modelling, Thermal and Enhanced Oil Recovery Processes)

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