Machine Learning Models With Engineering Insight: Case Studies From the Oil and Gas Industry

2021 ◽  
Author(s):  
Ishita Chakraborty ◽  
Daniel Kluk ◽  
Scot Mcneill
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Learning Models ◽  
Gas Industry ◽  
Machine Learning Models

Machine Learning Models With Engineering Insight: Case Studies From the Oil and Gas Industry

2020 ◽  
Author(s):  
Ishita Chakraborty ◽  
Daniel Kluk ◽  
Scot McNeill
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Mooring Line ◽  
Learning Models ◽  
Gas Industry ◽  
Position Data ◽  
Machine Learning Models

Abstract Machine learning is gaining rapid popularity as a tool of choice for applications in almost every field. In the oil and gas industry, machine learning is used as a tool for solving problems which could not be solved by traditional methods or for providing a cost-effective and faster data driven solution. Engineering expertise and knowledge of fundamentals remain relevant and necessary to draw meaningful conclusions from the data-based models. Two case studies are presented in different applications that will illustrate the importance of using engineering domain knowledge for feature extraction and feature manipulation in creating insightful machine learning models. The first case study involves condition-based monitoring (CBM) of pumps. A variety of pumps are employed in all aspects of the oilfield life cycle, such as drilling, completion (including hydraulic fracturing), production, and intervention. There is no well-established method to monitor the pump fault states as they are operating based on sensor feedback. As a result, maintenance is performed either prematurely or reactively, both of which result in wasteful downtime and unnecessary expense. A machine learning based neural network model is used for identifying different fault states in a triplex pump from measured pressure sensor data. In the second case study, failures of mooring lines of an offshore floating production unit are predicted from the vessel position data. Identifying a damaged mooring line can be critical for the structural health of the floating production system. In offshore floating platforms, mooring line tension is highly correlated to a vessel’s motions. The vessel position data is created from running coupled analysis models. A K-Nearest-Neighbor (KNN) classifier model is trained to predict mooring line failures. In all the case studies, the importance of combining a deep understanding of the physics of the problem with machine learning tools is emphasized.

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.

Complex Subsurface Well Abandonments Using Relief Well Methodologies

2021 ◽  
Author(s):  
Jamie Dorey ◽  
Georgy Rassadkin ◽  
Douglas Ridgway
Keyword(s):  
Case Studies ◽  
Best Practice ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Gas Seepage ◽  
Cost Efficient ◽  
Well Abandonment ◽  
Efficient Option ◽  
Practice Methods

Abstract The field experience in the continental US suggests that approximately 33% of plug and abandonment operations are non-routine, and 5% require re-entry (Greer C.R., 2018). In some scenarios, the most cost-efficient option for the intervention is drilling an intercept well to re-enter the target well or multiple wells externally using advanced survey management and magnetic ranging techniques. This paper presents the methods applied of relief well methodologies from the planning to execution of a complex multiple-well abandonment project. Improvements in Active Magnetic Ranging sensor design and applications have improved the availability of highly precise tools for the purpose of locating and intercepting wellbores where access is not possible. These instruments were commonplace on relief well interventions, however, have found a new application in solving one of the major issues facing the oil and gas industry. Subsurface abandonments are a complex task that requires a robust methodology. In this paper, we describe the techniques that have been built upon the best practices from industry experience (ISCWSA WISC eBook). This paper also illustrates how the combination of advanced survey management, gyro surveying, and magnetic ranging can be used following the best industry practices for fast and cost-efficient non-routine plug and abandonment. Case studies of several abandonment projects are presented showing the various technical challenges which are common on idle and legacy wells. The projects include wells that are currently under the ownership of an operator and orphaned wells that have been insufficiently abandoned and left idle over many decades. The case studies outline how the application of relief well methodologies to the execution of complex sub surface interventions led to the successful outcomes of meeting environmental and government regulations for wellbore abandonment. This includes performing multiple zonal isolations between reservoirs, water zones and preventing oil and gas seepage to the surface. The projects and their outcomes prove economically viable strategies for tackling the growing issue of idle and orphaned wells globally in a fiscally responsible manner. Combining industry best practice methods for relief well drilling, along with the technological advancements in magnetic ranging systems is a solution for one of the largest dilemmas facing the oil and gas industry in relation to idle and orphaned wellbores. These applications allow previously considered impossible abandonments to be completed with a high probability of long-term success in permanent abandonment.

Chapter 15. Human-Centered Concept Explanations for Neural Networks

2021 ◽  
Author(s):  
Chih-Kuan Yeh ◽  
Been Kim ◽  
Pradeep Ravikumar
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Case Studies ◽  
Real World ◽  
Deep Neural Networks ◽  
Learning Models ◽  
Real World Applications ◽  
The Right ◽  
Concept Activation ◽  
Machine Learning Models

Understanding complex machine learning models such as deep neural networks with explanations is crucial in various applications. Many explanations stem from the model perspective, and may not necessarily effectively communicate why the model is making its predictions at the right level of abstraction. For example, providing importance weights to individual pixels in an image can only express which parts of that particular image is important to the model, but humans may prefer an explanation which explains the prediction by concept-based thinking. In this work, we review the emerging area of concept based explanations. We start by introducing concept explanations including the class of Concept Activation Vectors (CAV) which characterize concepts using vectors in appropriate spaces of neural activations, and discuss different properties of useful concepts, and approaches to measure the usefulness of concept vectors. We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.

Subsurface engineering of CCUS in Australia (case studies)

2019 ◽  
Vol 59 (2) ◽  
pp. 762
Author(s):  
Mohammad B. Bagheri ◽  
Matthias Raab
Keyword(s):  
Uncertainty Analysis ◽  
Case Studies ◽  
Oil And Gas ◽  
Co2 Storage ◽  
Oil And Gas Industry ◽  
Reservoir Engineering ◽  
Co2 Injection ◽  
Gas Industry ◽  
Co2 Plume

Carbon capture utilisation and storage (CCUS) is a rapidly emerging field in the Australian oil and gas industry to address carbon emissions while securing reliable energy. Although there are similarities with many aspects of the oil and gas industry, subsurface CO2 storage has some unique geology and geophysics, and reservoir engineering considerations, for which we have developed specific workflows. This paper explores the challenges and risks that a reservoir engineer might face during a field-scale CO2 injection project, and how to address them. We first explain some of the main concepts of reservoir engineering in CCUS and their synergy with oil and gas projects, followed by the required inputs for subsurface studies. We will subsequently discuss the importance of uncertainty analysis and how to de-risk a CCUS project from the subsurface point of view. Finally, two different case studies will be presented, showing how the CCUS industry should use reservoir engineering analysis, dynamic modelling and uncertainty analysis results, based on our experience in the Otway Basin. The first case study provides a summary of CO2CRC storage research injection results and how we used the dynamic models to history match the results and understand CO2 plume behaviour in the reservoir. The second case study shows how we used uncertainty analysis to improve confidence on the CO2 plume behaviour and to address regulatory requirements. An innovative workflow was developed for this purpose in CO2CRC to understand the influence of each uncertainty parameter on the objective functions and generate probabilistic results.

From wells to decisions—data management for coal seam gas operators in Australia as compared to conventional oil and gas operators

2011 ◽  
Vol 51 (2) ◽  
pp. 716
Author(s):  
Peter Smith ◽  
Iain Paton
Keyword(s):  
Coal Seam ◽  
Data Management ◽  
Case Studies ◽  
Oil And Gas ◽  
Data Access ◽  
Oil And Gas Industry ◽  
Coal Seam Gas ◽  
Gas Industry ◽  
Innovative Solutions ◽  
Conventional Oil

The large number of wells associated with typical coal seam gas (CSG) developments in Australia has changed the paradigm for field management and optimisation. Real time data access, automation and optimisation—which have been previously considered luxuries in conventional resources—are key to the development and operation of fields, which can easily reach more than 1,000 wells. The particular issue in Australia of the shortage of skilled labour and operators has increased pressure to automate field operations. This extended abstract outlines established best practices for gathering the numerous data types associated with wells and surface equipment, and converting that data into information that can inform the decision processes of engineers and managers alike. There will be analysis made of the existing standard, tools, software and data management systems from the conventional oil and gas industry, as well as how some of these can be ported to the CSG fields. The need to define industry standards that are similar to those developed over many years in the conventional oil and gas industry will be discussed. Case studies from Australia and wider international CSG operations will highlight the innovative solutions that can be realised through an integrated project from downhole to office, and how commercial off the shelf solutions have advantages over customised one-off systems. Furthermore, case studies will be presented from both CSG and conventional fields on how these enabling technologies translate into increased production, efficiencies and lift optimisation and move towards the goal of allowing engineers to make informed decisions as quickly as possible. Unique aspects of CSG operations, which require similarly unique and innovative solutions, will be highlighted in contrast to conventional oil and gas.

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.

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