scholarly journals An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

ACS Omega ◽  
2021 ◽  
Author(s):  
Amjed Hassan ◽  
Murtada Saleh Aljawad ◽  
Mohamed Mahmoud
Keyword(s):  
Artificial Intelligence ◽  
Performance Prediction ◽  
Fractured Reservoirs ◽  
Naturally Fractured Reservoirs ◽  
Acid Fracturing ◽  
Naturally Fractured

Predicting the Productivity Enhancement After Applying Acid Fracturing Treatments in Naturally Fractured Reservoirs Utilizing Artificial Neural Network

2021 ◽  
Author(s):  
Amjed Mohamed Hassan ◽  
Murtada Saleh Aljawad ◽  
Mohamed Ahmed Mahmoud
Keyword(s):  
Neural Network ◽  
Correlation Coefficient ◽  
Fractured Reservoirs ◽  
Naturally Fractured Reservoirs ◽  
Percentage Error ◽  
Reservoir Properties ◽  
Productivity Improvement ◽  
Acid Fracturing ◽  
New Correlation ◽  
Naturally Fractured

Abstract Acid fracturing treatments are conducted to increase the productivity of naturally fractured reservoirs. The treatment performance depends on several parameters such as reservoir properties and treatment conditions. Different approaches are available to estimate the efficacy of acid fracturing stimulations. However, a limited number of models were developed considering the presence of natural fractures (NFs) in the hydrocarbon reservoirs. This work aims to develop an efficient model to estimate the effectiveness of acid fracturing treatment in naturally fractured reservoirs utilizing an artificial neural network (ANN) technique. In this study, the improvement in hydrocarbon productivity due to applying acid fracturing treatment is estimated, and the interactions between the natural fractures and the induced ones are considered. More than 3000 scenarios of reservoir properties and treatment parameters were used to build and validate the ANN model. The developed model considers reservoir and treatment parameters such as formation permeability, injection rate, natural fracture spacing, and treatment volume. Furthermore, percentage error and correlation coefficient were determined to assess the model prediction performance. The proposed model shows very effective performance in predicting the performance of acid fracturing treatments. A percentage error of 6.3 % and a correlation coefficient of 0.94 were obtained for the testing datasets. Furthermore, a new correlation was developed based on the optimized AI model. The developed correlation provides an accurate and quick prediction for productivity improvement. Validation data were used to evaluate the reliability of the new equation, where a 6.8% average absolute error and 0.93 correlation coefficient were achieved, indicating the high reliability of the proposed correlation. The novelty of this work is developing a robust and reliable model for predicting the productivity improvement for acid fracturing treatment in naturally fractured reservoirs. The new correlation can be utilized in improving the treatment design for naturally fractured reservoirs by providing quick and reliable estimations.

scholarly journals Development of Acid Fracturing Model for Naturally Fractured Reservoirs

2019 ◽  
Vol 34 (04) ◽  
pp. 735-748
Author(s):  
Assiya Ugursal ◽  
Ding Zhu ◽  
Alfred Daniel Hill
Keyword(s):  
Fractured Reservoirs ◽  
Naturally Fractured Reservoirs ◽  
Acid Fracturing ◽  
Naturally Fractured

Application of Artificial Intelligence to Characterize Naturally Fractured Reservoirs

2003 ◽  
Author(s):  
A. Kouider El Ouahed ◽  
Djebbar Tiab ◽  
Amine Mazouzi ◽  
Sarfraz A. Jokhio
Keyword(s):  
Artificial Intelligence ◽  
Fractured Reservoirs ◽  
Naturally Fractured Reservoirs ◽  
Naturally Fractured
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