Case Studies for the Value of Information and Flexibility in the Oil and Gas Industry

2021 ◽  
pp. 251-280
Author(s):  
Martin J. Vilela ◽  
Gbenga F. Oluyemi
Keyword(s):  
Case Studies ◽  
Value Of Information ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry

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.

Value of Information in the Oil and Gas Industry: Past, Present, and Future

2009 ◽  
Vol 12 (04) ◽  
pp. 630-638 ◽  
Author(s):  
Reidar B. Bratvold ◽  
J. Eric Bickel ◽  
Hans Petter Lohne
Keyword(s):  
Value Of Information ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Relevant Information ◽  
Information Gathering ◽  
Information Value ◽  
Analysis Tool ◽  
Well Test ◽  
Gas Industry ◽  
Decision Context

Summary An important task that petroleum engineers and geoscientists undertake is to produce decision-relevant information. Some of the most important decisions we make concern what type and what quality of information to produce. When decisions are fraught with geologic and market uncertainties, this information gathering may such forms as seismic surveys, core and well test analyses, reservoir simulations, market analyses, and price forecasts--which the industry spends billions of US dollars each year. Yet, considerably less time and resources are expended on assessing the profitability or value of this information. Why is that? This paper addresses how to make value-of-information (VOI) analysis more accessible and useful by discussing its past, present, and future. On the basis of a survey of SPE publications, we provide an overview of the use of VOI in the oil and gas industry, focusing on how the analysis was carried out and for which types of decisions VOI analysis has been performed. We highlight areas in which VOI methods have been used successfully and identify important challenges. We then identify and discuss the possible causes for the limited use of VOI methods and suggest ways to increase the use of this powerful analysis tool. Introduction One of the most useful features of decision analysis is its ability to distinguish between constructive and wasteful information gathering. VOI analysis evaluates the benefits of collecting additional information before making a decision. Such information gathering may be worthwhile if it holds the possibility of changing the decision that would be made without further information. VOI attributes no value to "uncertainty reduction" or "increased confidence" per se. Rather, value is added by enabling the decision maker (DM) to better "tune" his/her choice to the underlying uncertainty. Thus, information value is forever an entanglement of uncertainty and decision making; one cannot value information outside of a particular decision context.

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.

PRACTICAL DEVELOPMENTS IN PREDICTIVE OCEANOGRAPHY OF BENEFIT TO THE OIL AND GAS INDUSTRY

1994 ◽  
Vol 34 (1) ◽  
pp. 799
Author(s):  
Kerry Black
Keyword(s):  
Case Studies ◽  
Ocean Circulation ◽  
Oil And Gas ◽  
Produced Water ◽  
Oil Spills ◽  
Oil And Gas Industry ◽  
Basic Knowledge ◽  
Gas Industry ◽  
New Developments ◽  
Biological Responses

With improvements in equipment, satellite observations and basic knowledge, oceanographers are becoming more effective in predicting or hindcasting coastal and ocean circulation. Advantages which lead to better environmental outcomes for the oil and gas industry which arise from some of these improvements are discussed with case studies drawn from the Great Barrier Reef and Bass Strait. New developments in knowledge are particularly relevant to issues such as dispersal of produced water, oil spill modelling and environmental impact assessment, including a better understanding of the links between physical processes and biological responses. The case studies form part of a comprehensive hydrodynamic database developed on behalf of the Australian Maritime Safety Authority for an effective numerical modelling response to oil spills.

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.

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