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.

Assessment of Conflict Management in Niger Delta and Implications for Sustainable Development of Oil and Gas in Nigeria

2021 ◽  
Author(s):  
Humphrey Otombosoba Oruwari
Keyword(s):  
Conflict Management ◽  
Niger Delta ◽  
Oil And Gas ◽  
Ecological Factors ◽  
Oil And Gas Industry ◽  
Negative Effects ◽  
Gas Industry ◽  
Secondary Sources ◽  
Positive Elements ◽  
Oil And Gas Companies

Abstract Nigerian oil and gas industry have over the years witnessed incessant conflicts between the stakeholders, particularly the host communities in Niger Delta region and the oil and gas companies in partnership with the Federal Government. Conflict which is here defined as manifestation of disagreement between individual and groups arising from differing and mutually incompatible interests has both positive and negative effects depending on how it was managed. Managing conflicts is all about limiting the negative aspects. The study examined conflicts management in Nigeria oil and gas industry and how best the positive elements of conflicts can be maximally exploited for the mutual benefit of both oil and gas company and the host communities in Niger Delta. The study adopted the multidisciplinary approach, literature review, case study and relied on secondary sources using analytical method of data analysis. The study findings revealed that the major factors that precipitate conflicts between the oil and gas industry and host communities in Niger Delta include economic, social, political, and ecological factors. There are available strategies that can be used in conflict management. These include avoiding, accommodating, or smoothing, competing, or forcing, compromising, and collaborating. Any of these strategies can be used to manage conflict depending on the situation, the environment factor, and the nature of the conflict. The problem is that the oil and gas companies in partnership with the Nigerian government often adopted the wrong approach in dealing with the conflict with host communities, using avoiding or forcing strategies. The study recommends collaboration strategy which ensues long term-term solution to mutual benefits.

Numerical Investigation of Sand-Screen Performance in the Presence of Adhesive Effects for Enhanced Sand Control

SPE Journal ◽  
2019 ◽  
Vol 24 (05) ◽  
pp. 2195-2208 ◽  
Author(s):  
Siti Nur Shaffee ◽  
Paul F. Luckham ◽  
Omar K. Matar ◽  
Aditya Karnik ◽  
Mohd Shahrul Zamberi
Keyword(s):  
Management Practices ◽  
Oil And Gas ◽  
Voronoi Tessellation ◽  
Oil And Gas Industry ◽  
Solid Particles ◽  
Particle Adhesion ◽  
Sand Production ◽  
Gas Industry ◽  
Particle Filtration ◽  
Sand Control

Summary In many industrial processes, an effective particle–filtration system is essential for removing unwanted solids. The oil and gas industry has explored various technologies to control and manage excessive sand production, such as by installing sand screens or injecting consolidation chemicals in sand–prone wells as part of sand–management practices. However, for an unconsolidated sandstone formation, the selection and design of effective sand control remains a challenge. In recent years, the use of a computational technique known as the discrete–element method (DEM) has been explored to gain insight into the various parameters affecting sand–screen–retention behavior and the optimization of various types of sand screens (Mondal et al. 2011, 2012, 2016; Feng et al. 2012; Wu et al. 2016). In this paper, we investigate the effectiveness of particle filtration using a fully coupled computational–fluid–dynamics (CFD)/DEM approach featuring polydispersed, adhesive solid particles. We found that an increase in particle adhesion reduces the amount of solid in the liquid filtrate that passes through the opening of a wire–wrapped screen, and that a solid pack of particle agglomerates is formed over the screen with time. We also determined that increasing particle adhesion gives rise to a decrease in packing density and a diminished pressure drop across the solid pack covering the screen. This finding is further supported by a Voronoi tessellation analysis, which reveals an increase in porosity of the solid pack with elevated particle adhesion. The results of this study demonstrate that increasing the level of particle agglomeration, such as by using an adhesion–promoting chemical additive, has beneficial effects on particle filtration. An important application of these findings is the design and optimization of sand–control processes for a hydrocarbon well with excessive sand production, which is a major challenge in the oil and gas industry.

scholarly journals The impacts of corporate social resposibility practices on firm financial performance: Empirical evidence from Asian oil and gas industry

2021 ◽  
pp. 52-56
Author(s):  
Nguyen Thanh Dat Nguyen
Keyword(s):  
Financial Performance ◽  
Social Practice ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Asian Countries ◽  
Data Set ◽  
Gas Industry ◽  
Firm Financial Performance ◽  
Corporate Social ◽  
The Impact

The paper aims to investigate the impact of Corporate Social Responsibility (CSR) practices on the financial performance of oil and gas firms in Asian countries by using a panel data set that includes 23 firms from 7 Asian countries from 2004 to 2017. The empirical results support the research hypothesis that CSR practices have a negative impact on the financial performance of oil and gas companies. This means CSR practices may impose a substantial burden on firms in the oil and gas industry. In addition, we find that different CSR practices have different sizes of impact on firm financial performance. In particular, environment practice has the biggest impact, social practice ranks second, and governance practice has the weakest impact. The main results are also confirmed by several robustness tests.

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.

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.

Artificial Intelligence AI Assisted Thermography to Detect Corrosion Under Insulation CUI

2021 ◽  
Author(s):  
Ayman Amer ◽  
Ali Alshehri ◽  
Hamad Saiari ◽  
Ali Meshaikhis ◽  
Abdulaziz Alshamrany
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Oil And Gas ◽  
Assessment Tool ◽  
Oil And Gas Industry ◽  
Real Field ◽  
Learning Approach ◽  
Real Time System ◽  
Gas Industry ◽  
Corrosion Under Insulation

Abstract Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types. The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI. This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.

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