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Abstract The Chinarevskoye field in Kazakhstan consists of a significant number of producing gas and oil reservoirs from several stratigraphic zones, with additional discoveries being appraised for commercialty and undiscovered resources being matured for exploration drilling. Process facilities including oil treatment, gas handling, and compression and export facilities were commissioned at the start of commercial production and then more capacity was added recently. Detailed sub-surface models with risk modeling and uncertainties on and dependencies between geological parameters have been prepared to reflect the geological interpretations. The stochastic results based on Monte Carlo calculations provide distributions of in-place and recoverable resources. Similarly, detailed above-surface activity models with uncertainties on wells and facilities based on the sub-surface models have been prepared to model realistic development scenarios, including both existing and future production given capacity constraints. The stochastic results based on Monte Carlo calculations provide distributions of production profiles, cash flows, and net present values. Based on these results, the alternative scenarios with exploration, appraisal, development, and production activities have been ranked. The main conclusion is that investments in additional exploration activities and production facilities can be expected to increase the profitability of the Chinarevskoye field, even under the latest Kazakhstan fiscal terms. In addition the optimum exploration well-drilling sequence has been identified. The technical and economic assessments based on uncertainties and risks that have been ongoing over recent years have resulted in profitable strategic decisions for investments in additional exploration drilling, appraisal programs, and development of facilities for the Chinarevskoye field.
Abstract Digital Energy (DE) describes broad initiatives to improve asset performance and boost corporate value through operational excellence and engineering design. It has been described in many papers presented at SPE meetings such as Digital Energy in Houston and Intelligent Energy in Europe and the Middle East. Digital energy introduces new principles and information technology (IT) tools, frequently requiring new work processes, workforce adoption, and changes in behavior. One description of digital energy (Davidson and Lochmann 2011) includes: Fully-integrated, multi-disciplinary operations Task and process automation Digitally-enabled technology Business or operational intelligence Innovative, efficient methods to maximize performance The move from ‘good enough’ practices to operational excellence is a transformational change (evolving from one ‘look’ to another or one culture to another)and poses challenges and opportunities for organizations. Applying DE principles to promote operational excellence inevitably leads to new ways of working and unfortunately, organizational stress. Some of the challenges include aligning people, technology, and the organization to the new vision. The first step in the digital energy process, regardless of a project’s size, should bean assessment of an organization’s current state including its level of understanding of DE, readiness to change, and what a digital energy initiative may contain. An assessment is a way to look into the world of DE and identify the functionality that may be best suited for a company’s operating environment and assess its impact on performance. Assessments have proven essential for success when organizations undertook major projects to improve asset performance and increase corporate value. Assessments often uncover unexpected paths to better performance. The objective of an assessment is to identify and articulate the organization’s operational vision (different than broad IT or engineering objectives) and follow a structured approach to uncover and identify how DE initiatives can support and solidify the organization’s strategic objectives. During an assessment, information is gathered to identify and understand business drivers and goals while analyzing business priorities and developing value propositions. Through the evaluation and analysis of this information, a working solution roadmap is produced. Because E&P organizations are largely unfamiliar with the practical aspects of DE, the assessment step is often undervalued and, in some cases, skipped altogether. This means suboptimal results, increased project risk, and diminished returns.
- Europe (0.87)
- North America > United States > Texas (0.68)
- Energy > Power Industry (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (23 more...)
- Information Technology > Information Management (0.68)
- Information Technology > Data Science (0.68)
- Information Technology > Communications (0.46)
Abstract Declining global economic conditions and increased pressure to find new, affordable sources of energy create a turbulent macro environment for oil and gas companies. Stakeholders present conflicting goals of increased return on investment while simultaneously reducing risk and providing greater transparency into project performance. The traditional models of large scale investment into major capital projects can no longer guarantee success; instead, organizations must focus on capital management throughout a project's life cycle. Significant effort, time, and resources are invested in project delivery. However, poor decisions during the planning phases may result in loss of value during execution. By combining decision analysis tools and techniques with integrated, dynamic planning systems, companies are able to carefully manage their capital across the project life cycle to understand their costs, resources, and schedules. These companies benefit from increased collaboration leading to more informed decision making balancing risk and reward. This paper presents an innovative method for evaluating and dynamically planning the development of uncertain upstream investments. It centers on a paradigm shift in the way upstream managers assess investments, toward an approach that incorporates decision analysis tools and techniques with integrated dynamic planning systems. Case study examples are provided to illustrate key principles.
Abstract The Reserve Technical Potential Management System (RTPMS) is divided into 5 main stages and associated together with the Petroleum Reserve Management System by SPE. The main aim by merging these two concepts is to improve understanding of the reserves pool and therefore provide a practical guideline on a standard definition for high level planning and day-to-day operations. The concept is being developed with major focus on the rehabilitation of mature fields where each barrel of oil counts and uncertainty, both technical and economical, are becoming more challenging at industry level. High decline in productivity and increased operational challenges are common issues for mature fields. Recurrently, lower recovery factors are mainly driven by reservoir characterization uncertainty and management, geological complexity, limited resources and operational efficiency. This paper addresses some of these challenges in an integrated manner. Each stage is mapped and associated with the SPE Petroleum Reserve Management System, the project management control level, the time cycle (short: operation efficiency, medium : production optimization and long: reservoir management), a digital oilfield concept, the roles and responsibilities of the stakeholders, the technology groups and their key technologies. The five stages are defined as; 1) Actual Potential, 2) Operational Technical Potential, 3) Constrained Technical Potential, 4) Theoretical Technical Potential and 5) Ultimate Technical Potential. The concept has been developed based on lessons learnt and best practices acquired in mature field rehabilitation projects.
Abstract This paper tells the story of the steps that an E&P company took in implementing a methodology to plan, deliver and execute capital projects predictably. It addresses the challenges that any organization faces in effecting change, as well as the actions taken to ensure success for the initiative. This story can serve as a blueprint for any organization that wishes to improve capital performance. The focus of the paper is not so much the solution implemented, but rather the steps the organization took to ensure a successful implementation. In this paper, the terms "project delivery system" and "asset development system" will be used interchangeably. The company wanted to improve the predictability of its delivery of major capital projects. The company felt that to meet its business objectives, one of many things it needed to do was improve its capital execution or asset development performance. E&P companies have many paths to growth while achieving sustainability. A traditional approach is to grow via exploration while producing efficiently. Exploration and production are recognized critical competencies for E&P firms. What is often not appreciated is that the path from exploration to production must go through asset development or capital project execution. The figure below illustrates the three major competencies that an E&P company needs to grow. Often, firms choose the acquisition path in lieu of exploration. Nonetheless, short of acquiring fully mature assets, there is always some exploration work necessary
Improving Exploration, Appraisal & Pilot Planning through Better Forecasting of Uncertainty Reduction via Reliability of Information Interviewing and Confidence Plots
Coopersmith, E. M. (Decision Frameworks, L.P.) | Burkholder, M. K. (Decision Frameworks, L.P.) | Schulze, J. H. (Decision Frameworks, L.P.)
Abstract A challenge in the oil industry is the ability to forecast uncertainty reduction resulting from new information. This is particularly important in exploration, appraisal and pilot programs, where management weighs the amount of information expenditure needed to make drill or development decisions. Although a key decision criterion for this is often value, another important criterion is uncertainty reduction, or confidence in the interpretation of critical uncertainties. So how can teams understand project uncertainties and the effects of different information or appraisal options on their uncertainty assessments? Through an efficient, multi-discipline reliability of information assessment interview process designed to help the team think clearly and openly about the amount of uncertainty reduction to be gained for different information/appraisal programs. The product is a series of confidence graphs depicting uncertainty reduction versus information options. The plots are developed using Bayesian mathematics and the reliability of information assessment interviews. They address: (1) the probability of a correct interpretation; (2) the probability the actual outcome will be the low outcome after having forecast a P50 outcome; (3) the probability the actual outcome will be the low outcome after having forecast a high outcome; and (4) the probability of getting a given outcome after having correctly forecast that outcome from the information (e.g., the probability of getting a P50 outcome after forecasting a P50 outcome from a test). The important aspects of this paper are the set of reliability of interpretation questions used to forecast uncertainty reduction potential, how to conduct those interviews and how to develop the resulting confidence plots. The questions address the drivers of uncertainty reduction potential: information tool accuracy; effect of the environment from which the information is being collected; the tendency of the interpretation to better forecast true low, true P50 or true high states of nature; and the representativeness of the information.
Abstract With multi-billion dollar price tags for developments in deepwater, it is important to be prudent in reducing subsurface risk. But ultimately, the deepwater oil and gas business is a risky one, and sometimes the best course forward is to stop looking for unobtainable information and just proceed with development. And at $100+ million per well, deepwater appraisal is certainly not the place to be conducting science experiments. Subsurface experts are always hungry for more information, but this is not the realm of information for its own sake. Unless an appraisal well can affect key development decisions, it should probably be forgone. This paper reviews an actual application of a value of information (VOI) approach to putting a number on the value that each of several different appraisal wells could add to a potential development. The theoretical basis of this analysis has been covered by other authors -- the focus here is on the practical application of the method and advice on carrying it out. A VOI assessment is made on three proposed appraisal well targets in a discovered deepwater oil field. A standard application of VOI is used, using several key subsurface risks and uncertainties as the learning priorities for appraisal. These are built into a reservoir model and incorporated into an Experimental Design process to derive a regression equation for ultimate recoverable oil. Decision trees using discrete realizations as endpoints and an economic model are used to understand the various development decision paths with no additional information, with perfect information, and with imperfect information on the target uncertainties. The assessment of two of the three appraisal targets does not show enough information to change the original development plan. While the information does affect the estimate of ultimate recoverable oil, it does not change well placement, the optimal host capacity, or the basic Go/No-go decision on the development. A third target, penetrating multiple potential stacked reservoirs, does show the potential to materially change the development plan and is pursued. In the analysis, several factors are critical, including: clarity on the reference case decision with no additional information; clear benchmarks on threshold economic criteria; good reliability assessments and sensitivity testing; a good Design of Experiments process incorporating all key uncertainties; inputs from development planners and engineers to understand the impact of subsurface information on the development plan. The approach is able to put a dollar value on appraisal information and tie subsurface uncertainties directly back to critical development decisions. The analysis uses inputs that are all currently available or readily obtained from the project team. Most importantly, the analysis shifts the focus of appraisal from uncertainty to decisions, and this shift in perspective can have a profound effect on how a company approaches appraisal.
Abstract Most oil and gas executives and financial analysts have long believed that minimizing the time to first oil is one of the most important parameters to maximize the economic value of exploration and production (E&P) projects. This belief has driven project teams and oil company executives to push ever faster schedules. Our data show that chasing fast project schedules inadvertently destroys more value than it creates. We use a detailed database of oil and gas projects to conduct a rigorous statistical analysis, comparing project economics promised at sanction to the actual results achieved. Using performance data from the database, we can statistically quantify the change in expected outcomes (cost, production, reserves), and therefore the net present value (NPV) realized, as a result of different schedule targets. The results show that chasing aggressive first oil dates has a consistent negative effect on NPV because of worse than expected cost and production attainment. These effects are more damaging than the loss of value that occurs if a project is slowed down early in the project cycle to improve the quality of front-end preparation and planning that helps to mitigate cost and production attainment shortfalls. When speed becomes paramount, reservoir appraisal and project definition phases are shortened projects proceed without high-quality basic subsurface data, and often short-cut crucial planning phases. As the quality of data and planning degrades, teams are forced to make more assumptions, which increases uncertainty in cost, production, and reserves estimates. During execution, these major uncertainties, along with incomplete data and planning, drive cost growth, reserves downgrades, production shortfalls and, ironically, schedule slip. The poor than expected outcomes have a negative influence on the project economics, but are often ignored by economic models. In all projects there are choices to be made that lead to trade-offs between cost, schedule and production. Many companies prioritize their focus primarily on meeting their schedule and then, cost targets in order to achieve maximum economic returns. The reason production is often not part of the trade-off is because because of the belief that there is no trade-off between schedule and production, only between schedule and cost. Our analysis provides evidence to the contrary leading us to conclude that the order of priority should be reversed. We go beyond this observation and provide the reader with insights into how the unintended consequences of certain project drivers can be incorporated into more realistic economic models.
- North America > United States (0.46)
- North America > Canada (0.28)
An Efficient Decision Framework for Optimizing Tight and Unconventional Resources
Wehunt, C. D. (Chevron Corp.) | Hrachovy, M. J. (Chevron North America Exploration & Production Co.) | Walker, S. C. (Chevron North America Exploration & Production Co.) | Padmakar, A. S. (Chevron Energy Technology Co.)
Abstract Making an efficient and wise concept selection decision—quickly selecting the right project—is often of equal or greater importance than later design and execution tasks for determining project success. Value lost from a suboptimal concept selection decision or from a needlessly prolonged decision process is independent of value generation opportunities during design and execution, and cannot be recouped during later project phases. This paper presents decision framework and production forecasting processes that complement one another, and promote an efficient and high-quality concept selection decision for tight or unconventional resources. The method is for both oil and gas resources, and is especially useful for assessing and developing large contiguous tracts. High quality production forecasting is very important during concept selection. Better quality concept selection decisions will also result if the alternative conceptual plans are equally optimized when the decision is made, and our assessment process facilitates both accurate forecasting and equal optimization of the various development alternatives. Our method includes symmetry element reservoir simulation models and an efficient economic spreadsheet model with an optimizer. The sector simulation models run fast and can evaluate many cases, but they still explicitly address the physical effects relevant to flow in porous media with vertical, transverse, hydraulic fractures intersecting horizontal wells. The decision framework is structured so that some decisions are independent of the simulation model, and those decisions are rapidly optimized within the economic model. We introduce a fracture efficiency factor which may be important for modeling the diminished performance observed as the number of stages increase in multi-fractured horizontal wells. This fracture efficiency factor may also be an important discriminator of performance between wells fractured using aqueous vs. non-aqueous fracturing fluids. We also show how to use meaningful constraints with a symmetry element model to ensure that the economic forecasts are both realistic and achievable.
- North America > Canada (0.68)
- North America > United States > Colorado (0.67)
- North America > United States > Colorado > Skinner Ridge Field (0.99)
- North America > United States > Colorado > Piceance Basin > Williams Fork Formation (0.99)
- North America > Canada > British Columbia > Western Canada Sedimentary Basin > Horn River Basin > Horn River Shale Formation (0.99)
- (3 more...)
Abstract Maximizing Expected Present Value predicts that firms will seek 100% working interest in attractive ventures. However, firms frequently desire less than the entire working interest, and clearly pay much less than EPV to participate, management behavior often explained as a result of firms' risk aversion. Although there is little published evidence that firms routinely calculate Risk Adjusted Values (RAV), they provide a useful way to quantify risk aversion. This is not a new tool, as methods of deriving RAV and an Optimum Working Interest have been available in the literature for some time. Previous studies, which we review, have concentrated on defining a firm's Risk Tolerance, a necessary input in determining RAV. We have expanded the use of RAV in a number of ways. Firstly, we believe that a firm's RT is not constant, but varies by business unit to match the strategic direction of the firm. Secondly, we use actual and hypothetical examples to illustrate the use of RAV in (a) rationalizing the Fair Market Value of exploration portfolios, (b) defining the relative contributions of firms' exploration assets in mergers and acquisitions, (c) assessing farm-out values and targeting potential farminees, (d) selecting aligned partners for bidding groups and (e) government use for designing license rounds. Existing applications of RAV have usually been restricted to a simple lottery in which a single success leg is represented by mean success. This ignores the uncertainty in the success leg, with RAV being independent of the variance of the underlying reserves distribution. Through analysis and simulation, we review the sensitivity of RAV to reserves and value uncertainty. Introduction Decision-making theory states that when choosing between alternatives, the preferred option is the one which maximizes value. Where the alternatives include uncertainty, the value should be expressed as Certainty Monetary Equivalent (CME)[1]. It is usual to equate CME to Expected Monetary Value (EMV), the risk-weighted value. In oil industry evaluations we discount future cash flows at a discount rate to express value as PV. Thus exploration decisions revolve around the evaluation of Expected PV: EMV = EPV = WI * [(Ps * PVs) - (Pf * PVf)] …………….(1) where Ps is the probability of success, PVs is the full-cycle value of success for 100% working interest, PVf is the cost of failure for 100% working interest (and is therefore a positive number in this convention), and WI is the working interest. Pf is the probability of failure, the complement to Ps, which we retain as a separate variable although we could, of course, write it as (1 - Ps). The level of participation (WI) is one of the choices available when considering an exploration investment. Note that EMV increases linearly with increasing working interest. Thus the decision rule would maximize working interest, and the optimum working interest would always be 100%. Moreover, the price paid to purchase exploration opportunities should tend towards EPV in an efficient competitive market. In practice, the preferred working interest level is frequently less than 100%. Entry fees (signature bonuses, cash equivalents for farm-ins, and more esoteric effective purchase prices, like biddable fiscal terms) are substantially less than those that generate break-even EPV.
- Europe (0.68)
- North America > United States > Texas (0.46)