scholarly journals A systematic review of data science and machine learning applications to the oil and gas industry

2021 ◽  
Author(s):  
Zeeshan Tariq ◽  
Murtada Saleh Aljawad ◽  
Amjed Hasan ◽  
Mobeen Murtaza ◽  
Emad Mohammed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Analytical Solutions ◽  
Data Science ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Petroleum Exploration ◽  
Petroleum Engineering ◽  
Oil Well ◽  
Data Generation ◽  
Gas Industry

AbstractThis study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

Data Science Applied to Pedagogical Methodologies Focused on Changing the Negative Perception of the Oil and Gas Industry in Colombia

2020 ◽  
Author(s):  
Israel Guevara ◽  
David Ardila ◽  
Kevin Daza ◽  
Oscar Ovalle ◽  
Paola Pastor ◽  
...  
Keyword(s):  
Data Science ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Negative Perception

Boosting Algorithm Choice in Predictive Machine Learning Models for Fracturing Applications

2021 ◽  
Author(s):  
Abdul Muqtadir Khan
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Injection Rate ◽  
Model Construction ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Fracture Damage ◽  
Boosting Technique

Abstract With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing treatments. There are a variety of models available using various objective functions for optimization and different mathematical techniques. There is a need to extend the ML techniques to optimize the choice of algorithm. For fracturing treatment design, the literature for comparative algorithm performance is sparse. The research predominantly shows that compared to the most commonly used regressors and classifiers, some sort of boosting technique consistently outperforms on model testing and prediction accuracy. A database was constructed for a heterogeneous reservoir. Four widely used boosting algorithms were used on the database to predict the design only from the output of a short injection/falloff test. Feature importance analysis was done on eight output parameters from the falloff analysis, and six were finalized for the model construction. The outputs selected for prediction were fracturing fluid efficiency, proppant mass, maximum proppant concentration, and injection rate. Extreme gradient boost (XGBoost), categorical boost (CatBoost), adaptive boost (AdaBoost), and light gradient boosting machine (LGBM) were the algorithms finalized for the comparative study. The sensitivity was done for a different number of classes (four, five, and six) to establish a balance between accuracy and prediction granularity. The results showed that the best algorithm choice was between XGBoost and CatBoost for the predicted parameters under certain model construction conditions. The accuracy for all outputs for the holdout sets varied between 80 and 92%, showing robust significance for a wider utilization of these models. Data science has contributed to various oil and gas industry domains and has tremendous applications in the stimulation domain. The research and review conducted in this paper add a valuable resource for the user to build digital databases and use the appropriate algorithm without much trial and error. Implementing this model reduced the complexity of the proppant fracturing treatment redesign process, enhanced operational efficiency, and reduced fracture damage by eliminating minifrac steps with crosslinked gel.

Evaluation of Machine-Learning Tools for Predicting Sand Production

2021 ◽  
Author(s):  
Afungchwi Ronald Ngwashi ◽  
David O. Ogbe ◽  
Dickson O. Udebhulu
Keyword(s):  
Machine Learning ◽  
Niger Delta ◽  
Oil And Gas ◽  
Back Propagation ◽  
Oil And Gas Industry ◽  
Learning Tools ◽  
Sand Production ◽  
Data Set ◽  
Test Set ◽  
Gas Industry

Abstract Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing artificial intelligence and machine-learning tools to provide sustainable solutions to ever-increasing problems of the petroleum industry activities. Sand production is one of these real issues faced by the oil and gas industry. Understanding whether a well will produce sand or not is the foundation of every completion job in sandstone formations. The Niger Delta Province is a region characterized by friable and unconsolidated sandstones, therefore it's more prone to sanding. It is economically unattractive in this region to design sand equipment for a well that will not produce sand. This paper is aimed at developing a fast and more accurate machine-learning algorithm to predict sanding in sandstone formations. A two-layered Artificial Neural Network (ANN) with back-propagation algorithm was developed using PYTHON programming language. The algorithm uses 11 geological and reservoir parameters that are associated with the onset of sanding. These parameters include depth, overburden, pore pressure, maximum and minimum horizontal stresses, well azimuth, well inclination, Poisson's ratio, Young's Modulus, friction angle, and shale content. Data typical of the Niger Delta were collected to validate the algorithm. The data was further split into a training set (70%) and a test set (30%). Statistical analyses of the data yielded correlations between the parameters and were plotted for better visualization. The accuracy of the ANN algorithm is found to depend on the number of parameters, number of epochs, and the size of the data set. For a completion engineer, the answer to the question of whether or not a well will require sand production control is binary-either a well will produce sand or it does not. Support vector machines (SVM) are known to be better suited as the machine-learning tools for binary identification. This study also presents a comparative analysis between ANN and SVM models as tools for predicting sand production. Analysis of the Niger Delta data set indicated that SVM outperformed ANN model even when the training data set is sparse. Using the 30% test set, ANN gives an accuracy, precision, recall, and F1 - Score of about 80% while the SVM performance was 100% for the four metrics. It is then concluded that machine learning tools such as ANN with back-propagation and SVM are simple, accurate, and easy-to-use tools for effectively predicting sand production.

Enhanced Cement Composition for Preventing Annular Gas Migration

2019 ◽  
Author(s):  
George Kwatia ◽  
Mustafa Al Ramadan ◽  
Saeed Salehi ◽  
Catalin Teodoriu
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Oil Well ◽  
Gas Migration ◽  
Cement Slurry ◽  
Gas Industry ◽  
Well Integrity ◽  
Transit Times ◽  
Well Cement ◽  
Gas Tight

Abstract Cementing operations in deepwater exhibit many challenges worldwide due to shallow flows. Cement sheath integrity and durability play key roles in the oil and gas industry, particularly during drilling and completion stages. Cement sealability serves in maintaining the well integrity by preventing fluid migration to surface and adjacent formations. Failure of cement to seal the annulus can lead to serious dilemmas that may result in loss of well integrity. Gas migration through cemented annulus has been a major issue in the oil and gas industry for decades. Anti-gas migration additives are usually mixed with the cement slurry to combat and prevent gas migration. In fact, these additives enhance and improve the cement sealability, bonding, and serve in preventing microannuli evolution. Cement sealability can be assessed and evaluated by their ability to seal and prevent any leakage through and around the cemented annulus. Few laboratory studies have been conducted to evaluate the sealability of oil well cement. In this study, a setup was built to simulate the gas migration through and around the cement. A series of experiments were conducted on these setups to examine the cement sealability of neat Class H cement and also to evaluate the effect of anti-gas migration additives on the cement sealability. Different additives were used in this setup such as microsilica, fly ash, nanomaterials and latex. Experiments conducted in this work revealed that the cement (without anti-gas migration additive) lack the ability to seal the annulus. Cement slurries prepared with latex improved the cement sealability and mitigated gas migration for a longer time compared to the other slurries. The cement slurry formulated with a commercial additive completely prevented gas migration and proved to be a gas tight. Also, it was found that slurries with short gas transit times have a decent potential to mitigate gas migration, and this depends on the additives used to prepare the cement slurry.

scholarly journals Evolution of hydraulic fracturing fluid: from guar systems to synthetic gelling polymers

2021 ◽  
pp. 172-181
Author(s):  
A. P. Stabinskas ◽  
◽  
Sh. Kh. Sultanov ◽  
V. Sh. Mukhametshin ◽  
L. S. Kuleshova ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Industrial Development ◽  
Oil And Gas ◽  
Scale Up ◽  
Oil And Gas Industry ◽  
Chemical Components ◽  
Subsequent Stage ◽  
Oil Well ◽  
Gas Industry ◽  
Operational Activities

The paper presents the possibilities of optimizing technological approaches for performing hydraulic fracturing operations, taking into account the transition from traditionally used chemical components of the process fluid to synthetic gelling polymers. The proposed option makes it possible to reduce the unit costs of operational activities to increase oil production both for new assets of oil and gas producing companies and for assets at the stage of industrial development. The special emphasis of the proposed technological solutions is correlated with the environmental Agenda for Sustainable Development until 2030, aimed at transforming the production processes of the energy complex to reduce the ecological footprint of enterprises. A complete set of laboratory studies confirms the prospect of industrial application of synthetic polymer systems and the feasibility of replicating this approach. The subsequent stage of scale-up of pilot tests will allow to have a basis for development and implementation of standards in the oil and gas industry. Keywords: oil; well; hydraulic fracturing; chemicals; synthetic gelling polymers.

BUSINESS AND TAX ISSUES FOR PETROLEUM EXPLORATION AND DEVELOPMENT COMPANIES INVESTING IN THE UNITED STATES OF AMERICA

1991 ◽  
Vol 31 (1) ◽  
pp. 494
Author(s):  
Catherine A. Hayne
Keyword(s):  
United States ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
The United States ◽  
Detailed Examination ◽  
Petroleum Exploration ◽  
Gas Industry ◽  
Oil And Gas Exploration ◽  
Corporate Executives ◽  
Exploration And Production

Oil and gas exploration and production opportunities in the United States represent possibilities for investment by Australian petroleum companies in the 1990s. This paper focuses on the unique characteristics of the oil and gas industry, and is intended as an entrepreneurial guide to some of the practical business and tax issues which corporate executives will confront when proposing to do business in the United States. It provides a detailed examination of the key issues, but, due to the complexity of United States and Australian laws, this paper should not be used as a substitute for detailed advice.

Electrical Submersible Pump Prognostics and Health Monitoring Using Machine Learning and Natural Language Processing

2021 ◽  
Author(s):  
Rajeev Ranjan Sinha ◽  
Supriya Gupta ◽  
Praprut Songchitruksa ◽  
Saniya Karnik ◽  
Amey Ambade
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Learning Approaches ◽  
Submersible Pump ◽  
Gas Industry ◽  
Electrical Submersible Pump ◽  
Low Efficiency

Abstract Electrical Submersible Pump (ESP) systems efficiently pump high volumes of production fluids from the wellbore to the surface. They are extensively used in the oil and gas industry due to their adaptability, low maintenance, safety and relatively low environmental impact. They require specific operating conditions with respect to the power, fluid level and fluid content. Oilfield operation workflows often require extensive surveillance and monitoring by subject-matter experts (SMEs). Detecting issues like formation of unwanted gas and emulsions in ESPs requires constant analysis of downhole data by SMEs. The lack of adequate and accurate monitoring of the downhole pumps can lead to low efficiency, high lifting costs, and frequent repair and replacements. There are 3 workflows described in the paper which demonstrate that the maintenance costs of the ESPs can be significantly reduced, and production optimized with the augmentation of machine learning approaches typically unused in ESP surveillance and failure analysis.

Transdisciplinary Application of Functional Materials in Petroleum Engineering

2021 ◽  
Vol 1035 ◽  
pp. 649-654
Author(s):  
Gu Fan Zhao ◽  
Rui Yao Wang
Keyword(s):  
Information Technologies ◽  
Oil And Gas ◽  
New Technologies ◽  
Research Work ◽  
Functional Materials ◽  
Oil And Gas Industry ◽  
Petroleum Engineering ◽  
Densified Wood ◽  
Gas Industry ◽  
Literature Metrology

Currently, transdisciplinary integration has become increasingly close, and has gradually become the source of innovation. At the same time, petroleum engineering technologies demand more new technologies like functional materials and electronic information technologies. In order to effectively promote technological innovation and development of the petroleum engineering, it is important to continuously monitor, analyze and evaluate the latest development of the technologies outside of the oil and gas industry. This paper combines qualitative analysis of onsite demands, application cases, technical characteristics, and quantitative analysis of literature metrology, patent evaluation, technology maturity, to evaluate the application prospects of densified wood, liquid metal and poly (thioctic acid) in the field of petroleum engineering, and specific transdisciplinary suggestions are put forward. It is recommended to carry out pre-research work for the potential application of functional materials in the petroleum engineering, and it is expected to introduce new materials for downhole tools, new antennas for downhole instruments, extend long-term effectiveness of downhole plugging, and improve drilling efficiency.

Machine Learning as Accelerating Tool in Remote Operation Realisation through Monitoring Oil and Gas Equipments and Identifying its Failure Mode

2021 ◽  
Author(s):  
Ahmad Naufal Naufal ◽  
Samy Abdelhamid Samy ◽  
Nenisurya Hashim Nenisurya ◽  
Zaharuddin Muhammad Zaharuddin ◽  
Eddy Damsuri Eddy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Failure Mode ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
Environmental Damage ◽  
Equipment Failure ◽  
Statistical Regression ◽  
Gas Industry ◽  
Plant Equipment

Abstract Equipment failure, unplanned downtime operation, and environmental damage cost represent critical challenges in overall oil and gas business from well reservoir identification and drilling strategy to production and processing. Identifying and managing the risks around assets that could fail and cause redundant and expensive downtime are the core of plant reliability in oil and gas industry. In the current digital era; there is an essential need of innovative data-driven solutions to address these challenges, especially, monitoring and diagnosis of plant equipment operations, recognize equipment failure; avoid unplanned downtime; repair costs and potential environmental damage; maintaining reliable production, and identifying equipment failures. Machine learning-artificial intelligence application is being studied to develop predictive maintenance (PdM) models as innovative analytics solution based on real-data streaming to get to an elevated level of situational intelligence to guide actions and provide early warnings of impending asset failure that previously remained undetected. This paper proposes novel machine learning predictive models based on extreme learning/support vector machines (ELM-SVM) to predict the time to failure (TTF) and when a plant equipment(s) will fail; so maintenance can be planned well ahead of time to minimize disruption. Proper visualization with deep-insights (training and validation) processes of the available mountains of historian and real-time data are carried out. Comparative studies of ELM-SVM techniques versus the most common physical-statistical regression techniques using available rotating equipment-compressors and time-failure mode data. Results are presented and it is promising to show that the new machine learning (ELM-SVM) techniques outperforms physical-statistics techniques with reliable and high accurate predictions; which have a high impact on the future ROI of oil and gas industry.

Sign in / Sign up

Export Citation Format

Share Document