Study of the Effect of Rock Type and Treatment Parameter on Acid Fracture Conductivity Using an Intelligent Model

Akbari, Mohammadreza; Ameri, Mohammadjavad; Pournik, Maysam

doi:10.1007/s13369-016-2283-3

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…5. In another study by Akbari et al 24 , artificial neural network models were developed to predict fracture conductivity accurately. The models incorporated experimental data from various formations and showed good agreement between predictions and data.…”

Section: Figurementioning

confidence: 99%

“…Although an optimum point exists for achieving maximum fracture conductivity, it is difficult to determine because it varies by formation 24 . 6.…”

Section: Figurementioning

confidence: 99%

“…Therefore, the XGBoost model is considered to be superior to the other models in predicting rock conductivity. 24 is that machine learning methods generally perform better than deep learning methods when the dataset is small. This is because deep learning models tend to have many parameters and require a large amount of data to learn meaningful patterns and avoid overfitting.…”

Section: Comparison Of This Approach With Previous Correlations and I...mentioning

confidence: 99%

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A comparative study of fracture conductivity prediction using ensemble methods in the acid fracturing treatment in oil wells

Kharazi Esfahani,

Akbari,

Khalili

2024

Sci Rep

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The study of acid fracture conductivity stands as a pivotal aspect of petroleum engineering, offering a well-established technique to amplify production rates in carbonate reservoirs. This research delves into the intricate dynamics influencing the conductivity of acid fractures, particularly under varying closure stresses and in diverse rock formations. The conductivity of acid fractures is intricately interconnected with the dissolution of rock, etching patterns on fracture surfaces, rock strength, and closure stress. To accurately predict fracture conductivity under different closure stresses, a robust model is necessary. This model involves assessing both the baseline fracture conductivity under zero closure stress and the rate of conductivity variation as closure stress fluctuates. Key among the influential factors affecting fracture conductivity is the type of rock within the reservoir. Understanding and predicting the behavior of different formations under disparate closure stresses poses a significant challenge, as does deciphering the diverse effects of treatment parameters such as acid injection rate and strength on fracture conductivity. In this study, the predictive power of XGBoost, a machine learning algorithm, was explored in assessing acid fracture conductivity in dolomite and limestone formations. The findings revealed XGBoost's ability to outperform previous studies in predicting fracture conductivity in both types of formations. Notably, it exhibited superior accuracy in forecasting fracture conductivity under varying treatment conditions, underscoring its robustness and versatility. The research underscores the pivotal role of closure stress, dissolution rate of rock (DREC), and rock strength in influencing fracture conductivity. By integrating these parameters into the design of acid fracturing operations, accurate predictions can be achieved, allowing for the optimization of treatment designs. This study illuminates the potential of XGBoost in optimizing acid fracturing treatments, ultimately bolstering well productivity in carbonate reservoirs. Furthermore, it advocates for the essential nature of separate modeling and analysis based on rock types to comprehend and optimize fracturing processes. The comparison between dolomite and limestone formations unveiled distinct conductivity behaviors, underlining the significance of tailored analyses based on rock type for precise operational optimization.

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Section: Figurementioning

confidence: 99%

“…Although an optimum point exists for achieving maximum fracture conductivity, it is difficult to determine because it varies by formation 24 . 6.…”

Section: Figurementioning

confidence: 99%

Section: Comparison Of This Approach With Previous Correlations and I...mentioning

confidence: 99%

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A comparative study of fracture conductivity prediction using ensemble methods in the acid fracturing treatment in oil wells

Kharazi Esfahani,

Akbari,

Khalili

2024

Sci Rep

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“…Acid-etched fracture conductivity is one of the most important evaluation criteria for the acid fracturing effect. 27,28 The acidetched fracture conductivity test procedure (the theoretical analysis of the experiment refers to the work by Asadollahpour et al 27 ) was performed using the acid-etched fracture conductivity equipment (Fig. 4) produced by Chengdu Core Technology Co., Ltd in Sichuan Province to evaluate the performance of the EA.…”

Section: Etched Fracture Conductivity Test Of the Eamentioning

confidence: 99%

A reservoir-damage-free encapsulated acid dually controlled by hydrogen ion concentration and temperature

et al. 2019

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“…Both domestic and foreign scholars have undertaken studies on the mechanical properties of rocks under the action of mine water. Hu 4 , Guo 5 , Zhou 6 and Akbari 7 conducted experimental studies on the microscopic variability of mudstone in the deep coal seam's bottom plate, subjected to infiltration by distilled water, tap water, and mine water. The impact of mine water on the conductivity and disintegration of mudstone surpasses that of distilled water and tap water.…”

Section: Introductionmentioning

confidence: 99%

Experimental research on compressive strength deterioration of coal seam floor sandstone under the action of acidic mine drainage

Han,

Chen,

Liu

et al. 2024

Sci Rep

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In sulphur-coal symbiotic coal seams, after the mining of sulphide iron ore, when the coal resources are mined, the mine water accumulated in the roadway mining area will have a certain impact on the stability of the surrounding rock of the coal seam roadway. Taking the floor sandstone of sulfur coal symbiotic coal seam as the research object, the roof fissure water with pH values of 7.48, 4.81 and 2.62 was used as the experimental solution. 10 experimental schemes were designed to measure the compressive strength of the samples under the action of AMD, and the hydrochemical analysis of AMD was conducted. The pore structures of the samples before and after the action of AMD were analyzed. Based on the hydrochemistry and pore structure, the deterioration mechanism of compressive strength of the coal seam floor sandstone under the action of AMD was explained. The results indicated that the compressive strength of the samples decreased with the increasing action time of AMD. The compressive strength decreased with the increment of the porosity. The concentration of H+ ion in AMD was relatively small. Na2O in albite dissolved and reacted with water, leading to an increase in the concentration of Na+ ion. Soluble substances such as MgCl2 and CaSO4 in the pore structure dissolved, leading to an increase in the concentration of Ca2+ and Mg2+ ions. The dissolution of soluble substances and the physical–chemical reactions between solutions and minerals were the essential causes of the continuous deterioration of the compressive strength of the coal seam floor sandstone. The results of this study can provide a theoretical basis for the deterioration of the mechanical properties of the peripheral rock in the roadway of the sulphur coal seam, and can also provide a certain engineering reference for the sulphur coal seam roadway.

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Study of the Effect of Rock Type and Treatment Parameter on Acid Fracture Conductivity Using an Intelligent Model

Cited by 6 publications

References 17 publications

A comparative study of fracture conductivity prediction using ensemble methods in the acid fracturing treatment in oil wells

A comparative study of fracture conductivity prediction using ensemble methods in the acid fracturing treatment in oil wells

A reservoir-damage-free encapsulated acid dually controlled by hydrogen ion concentration and temperature

Experimental research on compressive strength deterioration of coal seam floor sandstone under the action of acidic mine drainage

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