Advances in Computational Intelligence and Machine Learning Techniques for Exploration and Production in the Oil and Gas Industry

Dear Colleagues,

Over the last decades, computational intelligence and machine learning have been increasingly used in the geosciences and especially in the oil and gas industry at exploration and production sectors. These technologies are capable of processing and analysing a large volume of information, acting in the construction of geological models and providing applicable results in rapid decision-making and in increasing the efficiency and economy of the oil and gas industry. Computational Intelligence and Machine Learning models have been incorporated recently in almost all stages of the process. Models based on Deep Learning, such as Convolutional Neural Networks, Recurrent Neural Networks and Autoencoders, had obtained promising results in different tasks from evaluating the probability of exploratory success of new prospects, seismic interpretation, seismic inversion, lithofacies classification, production prediction and optimization, planning and optimization of drilling operations and machinery & systems monitoring and maintenance.

This Special Issue will collect original research or review articles on the recent development of Computational Intelligence and Machine Learning techniques applied to Exploration and Production (E&P) in the Oil and Gas Industry. The preferred subjects for the Special Issue include items like geological risk assessment, reservoir characterization, seismic interpretation (including horizons picking, facies classification, fault and salt domes detection and others), seismic inversion, production history matching, time-lapse inversion, planning and optimization of drilling and completion operations, digital twins for systems monitoring and maintenance. Any Computational Intelligence and Machine Learning research topic contributing to  the  energy area  (focusing  in  the  oil  and  gas  sector)  are welcome.

Prof. Dr. Mauro Roisenberg
Prof. Dr. Wagner Moreira Lupinacci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• computational intelligence and machine learning
• deep learning
• seismic interpretation
• seismic inversion
• production forecasting and optimization
• predictive maintenance