Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs

Tewari, Saurabh; Dwivedi, U.D.

doi:10.1016/j.cie.2018.08.018

Cited by 58 publications

(15 citation statements)

References 43 publications

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“…ANN has been reported to have a tendency to get stuck in local minima, which is why it failed to predict the correct bit type for certain target formations [19,48]. When ANN is combined with GA, the optimization task is handled by GA, which is a strong optimizer and converses to the correct BT except in zone 1, as compared to actual BT.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Drilling of Oil and Gas Wells Using Response Surface Methodology and Artificial Bee Colony

2021

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The oil and gas industry plays a vital role in meeting the ever-growing energy demand of the human race needed for its sustainable existence. Newer unconventional wells are drilled for the extraction of hydrocarbons that requires advanced innovations to encounter the challenges associated with the drilling operations. The type of drill bits utilized in any drilling operation has an economical influence on the overall drilling operation. The selection of suitable drill bits is a challenging task for driller while planning for new wells. Usually, when it comes to deciding the drill bit type, generally, the data of previously drilled wells present in similar geological formation are analyzed manually, making it subjective, erroneous, and time consuming. Therefore, the main objective of this study was to propose an automatic data-driven bit type selection method for drilling the target formation based on the Optimum Penetration Rate (ROP). Response Surface Methodology (RSM) and Artificial Bee Colony (ABC) have been utilized to develop a new data-driven modeling approach for the selection of optimum bit type. Data from three nearby Norwegian wells have been utilized for the testing of the proposed approach. RSM has been implemented to generate the objective function for ROP due to its strong data-fitting characteristic, while ABC has been utilized to locate the global optimal value of ROP. The proposed model has been generated with a 95% confidence level and compared with the existing model of Artificial Neural Network and Genetic Algorithm. The proposed approach can also be applied over any other geological field to automate the drill bit selection, which can minimize human error and drilling cost. The United Nations Development Programme also promotes innovations that are economical for industrial sectors and human sustainability.

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Section: Discussionmentioning

confidence: 99%

Intelligent Drilling of Oil and Gas Wells Using Response Surface Methodology and Artificial Bee Colony

2021

Self Cite

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“…In terms of its attributes, big data was first defined with three V's, i.e., volume, velocity, and variety [114]. Over the years, however, more attributes have been added, such as veracity, variability, volatility, and value, to make it seven V's [115]. There are currently 42 V's identified for big data [116] that goes to show a higher sophistication level.…”

Section: Big Data Analyticsmentioning

confidence: 99%

A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Butt

2020

Designs

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Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition.

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“…Consequently, many studies have been undertaken that use AI and ML methods to better classify reservoir recovery factor. ANN, genetic algorithm (GA), support vector regression (SVR), and fuzzy logic are some of the AI/ML methods deployed [33][34][35][36]. These methods have been used in the petroleum industry to improve, discover, and quantify a variety of properties, leading to remarkable results in terms of reservoir characterization, rock identification, anomaly detection, and stranded drill pipe classification [37].…”

Section: Related Workmentioning

confidence: 99%

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

Al-Tashi

Akhir

Abdulkadir

et al. 2021

JMSE

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The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate.

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Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs

Cited by 58 publications

References 43 publications

Intelligent Drilling of Oil and Gas Wells Using Response Surface Methodology and Artificial Bee Colony

Intelligent Drilling of Oil and Gas Wells Using Response Surface Methodology and Artificial Bee Colony

A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

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