A comparative study of brine solutions as completion fluids for oil and gas fields.

Completion fluids play a vital role in well-related processes within the oil extraction industry. This article presents a comprehensive study of the properties and performance of various brine solutions as completion fluids for different well and reservoir conditions. Attributes examined include density, corrosion resistance, temperature stability, compatibility with formation fluids, clay swelling potential and influence on wettability. The research highlights the significance of selecting appropriate completion fluids to optimize well and reservoir operations. Zinc chloride emerges as an excellent option for high density applications, while sodium chloride and potassium formate solutions are ideal for extreme cold conditions. Potassium acetate outperforms calcium chloride and potassium chloride and has excellent pH stability. The compatibility of completion fluids with formation water has been observed to be excellent, with no sedimentation or emulsion formation. Potassium acetate also experiences minimal clay swelling, making it suitable for clay-rich formations. On the other hand, calcium chloride has a higher clay swelling than most of the brines tested, making it less suitable for sandstone formations with a higher clay content than these brines. The research evaluates the water-wetting abilities of completion fluids in carbonate and sandstone formations. Potassium chloride and zinc chloride have the most significant impact in carbonate formations, while potassium acetate and potassium formate excel in sandstone formations. This study provides a comprehensive understanding of completion fluids, facilitating informed decisions that maximize operational efficiency, protect reservoir integrity, and enhance hydrocarbon recovery. The appropriate selection of completion fluids should align with specific well and reservoir conditions, considering the priorities of the application.

A comprehensive analysis of carbonate matrix acidizing using viscoelastic diverting acid system in a gas field.

This paper explores matrix acidizing, a method to enhance well productivity by injecting acid into the formation to dissolve damage or create flow channels. Focusing on gas well acidizing, it introduces a groundbreaking three-stage approach with hydrochloric acid (HCl) and viscoelastic diverting acid (VDA). Unlike recent research, which often overlooked specific VDA stages and favored VES or surfactant gelled systems, this study innovatively integrates VDA throughout laboratory experimentation, simulation modeling, and operational execution. The article showcases the effectiveness of HCl and VDA in dissolving reservoir materials, preventing issues like emulsion formation and iron precipitation, reducing corrosion and H2S emissions, enhancing penetration depth, fluid flow channels, and stimulating all reservoir layers. Utilizing a numerical model, it recommends an optimal acidizing method with five main acid injection stages and five VDA injection stages. The results demonstrate a notable increase of 100% in gas production, an 84% rise in gas pressure, and a reduction of BS&W from 7 to 3%. Aimed at industry professionals, this paper serves as a guide for optimizing well productivity and gas recovery processes.

Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance analysis.

Interfacial tension (IFT) is a key physical property that affects various processes in the oil and gas industry, such as enhanced oil recovery, multiphase flow, and emulsion stability. Accurate prediction of IFT is essential for optimizing these processes and increasing their efficiency. This article compares the performance of six machine learning models, namely Support Vector Regression (SVR), Random Forests (RF), Decision Tree (DT), Gradient Boosting (GB), Catboosting (CB), and XGBoosting (XGB), in predicting IFT between oil/gas and oil/water systems. The models are trained and tested on a dataset that contains various input parameters that influence IFT, such as gas-oil ratio, gas formation volume factor, oil density, etc. The results show that SVR and Catboost models achieve the highest accuracy for oil/gas IFT prediction, with an R-squared value of 0.99, while SVR outperforms Catboost for Oil/Water IFT prediction, with an R-squared value of 0.99. The study demonstrates the potential of machine learning models as a reliable and resilient tool for predicting IFT in the oil and gas industry. The findings of this study can help improve the understanding and optimization of IFT forecasting and facilitate the development of more efficient reservoir management strategies.

Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability.

Formation damage poses a widespread challenge in the oil and gas industry, leading to diminished permeability, flow rates, and overall well productivity. Acidizing is a commonly employed technique aimed at mitigating damage and enhancing permeability. In this study, to predict the permeability after acidizing in oil and gas reservoirs, three machine learning models, namely artificial neural networks, random forest, and XGBoost, along with genetic programming were used to estimate permeability changes after acidizing. These models are utilized to estimate permeability changes following acidizing operations. Training of the models involved a dataset comprising 218 acidizing operations conducted in diverse reservoirs across Iran. The input parameters, namely permeability, porosity, skin factor, calcite mineral fraction, acid injection rate, and injected acid volume, were optimized through the use of a genetic algorithm. Statistical and graphical analysis of the results demonstrates that genetic programming outperformed the other machine learning techniques, yielding superior performance with R square and RMSE values of 0.82 and 17.65, respectively. Nevertheless, the other models also exhibited commendable performance, surpassing an R square value of 0.73. The post-acidizing permeability data obtained from core flooding experiments conducted on carbonate and sandstone cores was utilized to validate the models. The genetic programming model demonstrates an average error of 21.1%. The evaluation of post-acidizing permeability using genetic programming, in comparison with the results obtained from the core-flood test, revealed errors of 22.95% and 32.4% for carbonate and sandstone cores, respectively. Furthermore, a comparison between the calculated post-acidizing permeability derived from the GP model and previous studies indicated errors within the range of 8.6-26.59%. The findings highlight the potential of genetic programming and machine learning algorithms in accurately predicting post-acidizing permeability, thereby aiding in acidizing design, effectiveness assessment, and ultimately enhancing oil and gas production rates.