Application of Artificial Intelligence Techniques to Predict the Well Productivity of Fishbone Wells

Hassan, Amjed; Elkatatny, Salaheldin; Abdulraheem, Abdulazeez

doi:10.3390/su11216083

Cited by 19 publications

(11 citation statements)

References 24 publications

(25 reference statements)

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“…Then, the rest of the data (30%), which was unseen during the training stage, was used to evaluate the model performance. The use of 70 and 30% of the data for training and testing, respectively, was reported by several researchers; ,− therefore, we used these ratios in the current study. Moreover, several evaluation indices were used to evaluate the model’s reliability.…”

Section: Methodsmentioning

confidence: 99%

“…Also, mathematical correlations can be developed from the optimized models, especially ANN models. The mathematical correlations will allow easy and direct applications for the developed models. , Moreover, AI models can be utilized to improve the computational efficiency of complex fractures’ models. Artificial intelligence models can reduce the time required to estimate the fracture performance by several orders of magnitude. , Therefore, and because of the above-mentioned advantages of artificial intelligence techniques, we employed several AI tools in this work to provide quick and reliable estimations for the fracture conductivity.…”

Section: Introductionmentioning

confidence: 99%

“…The mathematical correlations will allow easy and direct applications for the developed models. 15,17 Moreover, AI models can be utilized to improve the computational efficiency of complex fractures' models. Artificial intelligence models can reduce the time required to estimate the fracture performance by several orders of magnitude.…”

Section: ■ Introductionmentioning

confidence: 99%

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

2021

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Acid fracturing is one of the most effective techniques for improving the productivity of naturally fractured carbonate reservoirs. Natural fractures (NFs) significantly affect the design and performance of acid fracturing treatments. However, few models have considered the impact of NFs on acid fracturing treatments. This study presents a simple and computationally efficient model for evaluating acid fracturing efficiency in naturally fractured reservoirs using artificial intelligence-based techniques. In this work, the productivity enhancement due to acid fracturing is determined by considering the complex interactions between natural and hydraulic fractures. Several artificial intelligence (AI) techniques were examined to develop a reliable predictive model. An artificial neural network (ANN), a fuzzy logic (FL) system, and a support vector machine (SVM) were used. The developed model predicts the productivity improvement based on reservoir permeability and geomechanical properties (e.g., Young’s modulus and closure stress), natural fracture properties, and design conditions (i.e., acid injection rate, acid concentration, treatment volume, and acid types). Also, several evaluation indices were used to evaluate the model reliability including the correlation coefficient, average absolute percentage error, and average absolute deviation. The AI model was trained and tested using more than 3100 scenarios for different reservoir and treatment conditions. The developed ANN model can predict the productivity improvement with a 3.13% average absolute error and a 0.98 correlation coefficient, for the testing (unseen) data sets. Moreover, an empirical equation was extracted from the optimized ANN model to provide a direct estimation for productivity improvement based on the reservoir and treatment design parameters. The extracted equation was evaluated using validation data where a 4.54% average absolute error and a 0.99 correlation coefficient were achieved. The obtained results and degree of accuracy show the high reliability of the proposed model. Compared to the conventional simulators, the developed model reduces the time required for predicting the productivity improvement by more than 60-fold; therefore, it can be used on the fly to select the best design scenarios for naturally fractured formations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…Management and effective resource scheduling take a significant amount of time, which naturally shifts the focus of construction projects to new technologies (Tiencheu et al 2021 ; Ferreira et al 2019 ). It is because there is always a substantial need for automation in the construction industry, as it diverts the time and attention of human resources to the most critical activities that need to be completed on time (Hassan et al 2019 ). Routine management work is entirely handled by the modern systems in which any level of AI is implemented.…”

Section: Research Article Citation Trendmentioning

confidence: 99%

Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

et al. 2023

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Oil and gas construction projects are critical for meeting global demand for fossil fuels, but they also present unique risks and challenges that require innovative construction approaches. Artificial Intelligence (AI) has emerged as a promising technology for tackling these challenges, and this study examines its applications for sustainable development in the oil and gas industry. Using a systematic literature review (SLR), this research evaluates research trends from 2011 to 2022. It provides a detailed analysis of how AI suits oil and gas construction. A total of 115 research articles were reviewed to identify original contributions, and the findings indicate a positive trend in AI research related to oil and gas construction projects, especially after 2016. The originality of this study lies in its comprehensive analysis of the latest research on AI applications in the oil and gas industry and its contribution to developing recommendations for improving the sustainability of oil and gas projects. This research’s originality is in providing insight into the most promising AI applications and methodologies that can help drive sustainable development in the oil and gas industry.

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“…The application of machine learning strategies has been widely practiced in the oil and gas development. These attempts have covered aspects of enhanced oil recovery [7][8][9][10][11][12][13][14], fracture detection [15], development plan optimization [15,16], dynamic production prediction [18][19][20][21] and asphaltene precipitation prediction [22]. Some studies have also focused on applying machine learning strategies to model permeability impairment due to mineral scale deposition [23][24][25] and predict the success of an inhibition scenario in the field [4].…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

Fazelabdolabadi

Montazeri

Pourafshary

2021

HighTech. Innov. J.

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The production of hydrocarbon resources at an oil field is concomitant with challenges with respect to the formation of scale inside the reservoir rock – intricately impairing its permeability and hindering the flow. Historically, the effect of ions is attributed to the undergone phenomenon; nevertheless, there exists a great deal of ambiguity about its relative significance compared to other factors, or the effectiveness as per the ion type. The present work applies a data mining strategy to unveil the influencing hierarchy of the parameters involved in driving the process within major rock categories – sandstone and carbonate – to regulate a target functionality. The functionalities considered evolve around maximizing the oil recovery, minimizing permeability impairment/ scale damage. A pool of experimental as well as field data was used for this sake, accumulating the bulk of the available literature data. The methods used for data analysis in the present work included the Bayesian Network, Random Forest, Deep Neural Network, as well as Recursive Partitioning. The results indicate a rolling importance for different ion species - altering under each functionality – which is not ranked as the most influential parameter in either case. For the oil recovery target, our results quantify a distinction between the source of ion of a single type, in terms of its influencing rank in the process. This latter deduction is the first proposal of its kind – suggesting a new perspective for research. Moreover, the machine learning methodology was found to be capable of reliably capturing the data – evidenced by the minimal errors in the bootstrapped results. Doi: 10.28991/HIJ-2021-02-03-05 Full Text: PDF

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Application of Artificial Intelligence Techniques to Predict the Well Productivity of Fishbone Wells

Cited by 19 publications

References 24 publications

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

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

Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

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