Quantifying 3D Land Seismic Reliability and Value
Abstract: TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractSeismic data provide important information for guiding reservoir development. Improvements in data quality hold the promise of improving performance even further. Yet, the value of these data, and decision making capability, must exceed its cost. Previous work has demonstrated value of information (VOI) methods to quantify the value of seismic data. In these examples, seismic accuracy is obtained via expert assessment instead of being based on geophysical qua… Show more
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Cited by 3 publications
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This study examines the loss in project value incurred when concept selection decisions are based on erred estimates of input variables. Estimates of the magnitude of such losses are provided, along with an analysis of which input estimates matter most in determining value loss. A procedure for concept selection is defined to model the decision making process and is used in conjunction with a simplified asset development optimization model to estimate project values. The analysis compares project values resulting from concept selection decisions based on erred estimates and decisions based on an alternate hypothesis. Results suggest that cost of erred estimates for initial costs, expansion costs, and the timing of future expansion projects are comparable in magnitude to the cost of erred reserve estimates. Also, the cost of underestimating expected reserve volume tends to be larger than the cost of overestimating reserve volume; aggressive cost estimates are more destructive to value than conservative estimates; and conservative schedule estimates for the timing of expansion projects are generally more destructive to value than aggressive schedule estimates. Introduction During the concept comparison and selection phase of exploration and production (E&P) capital projects, decision makers estimate the value of competing development concepts. These estimates are used to rank options and to select one option to carry forward to the next project phase. The importance of these estimates cannot be overstated, they determine which concept is selected, and have a strong influence on field architecture, initial capacity of facilities, well counts, production rates, and project schedule. Decisions in concept selection have a large impact on the value ultimately derived from the asset.1,2 These estimates are also used for other important analyses and decisions during concept selection such as value of information (VOI) analysis. A variety of input variables are required to estimate the value of competing development concepts. These input variables include estimates for the subsurface (e.g. reserves, flow rates, decline rates), estimates for the surface facilities (e.g. CAPEX, OPEX, schedule, reliability), and estimates for exogenous factors such as commodity price. The true values of these input variables are almost always unknown, and estimates are developed based on the current information set available to the decision maker. The objective of this study is to examine and compare the loss in value incurred when concept selection decisions are based on erred estimates of input variables. Errors can occur in estimates of expected values and in estimates of variance. The conclusion that erred estimates of input variables destroy project value can be made using common sense. What this study attempts to provide are original estimates of the potential magnitude of such losses, and an analysis of which input estimates matter more or less than others. In practice, one does not know if a current estimate for an input variable is erred, but one can estimate the impact of an alternate hypothesis being true, and this is the framework adopted here. A procedure for concept selection is defined to model the decision making process and is used in conjunction with a simplified asset development optimization model to estimate project values. The analysis compares project values resulting from concept selection decisions based on erred estimates and decisions based on an alternate hypothesis; in both cases, the alternate hypothesis is taken to be true. The difference in value observed, if any, is caused by sub-optimal initial facility capacity (note, the difference in value can also be interpreted as the maximum willingness to pay to confirm the alternate hypothesis). The approach is similar in form to standard VOI analyses.3–6
This study examines the loss in project value incurred when concept selection decisions are based on erred estimates of input variables. Estimates of the magnitude of such losses are provided, along with an analysis of which input estimates matter most in determining value loss. A procedure for concept selection is defined to model the decision making process and is used in conjunction with a simplified asset development optimization model to estimate project values. The analysis compares project values resulting from concept selection decisions based on erred estimates and decisions based on an alternate hypothesis. Results suggest that cost of erred estimates for initial costs, expansion costs, and the timing of future expansion projects are comparable in magnitude to the cost of erred reserve estimates. Also, the cost of underestimating expected reserve volume tends to be larger than the cost of overestimating reserve volume; aggressive cost estimates are more destructive to value than conservative estimates; and conservative schedule estimates for the timing of expansion projects are generally more destructive to value than aggressive schedule estimates. Introduction During the concept comparison and selection phase of exploration and production (E&P) capital projects, decision makers estimate the value of competing development concepts. These estimates are used to rank options and to select one option to carry forward to the next project phase. The importance of these estimates cannot be overstated, they determine which concept is selected, and have a strong influence on field architecture, initial capacity of facilities, well counts, production rates, and project schedule. Decisions in concept selection have a large impact on the value ultimately derived from the asset.1,2 These estimates are also used for other important analyses and decisions during concept selection such as value of information (VOI) analysis. A variety of input variables are required to estimate the value of competing development concepts. These input variables include estimates for the subsurface (e.g. reserves, flow rates, decline rates), estimates for the surface facilities (e.g. CAPEX, OPEX, schedule, reliability), and estimates for exogenous factors such as commodity price. The true values of these input variables are almost always unknown, and estimates are developed based on the current information set available to the decision maker. The objective of this study is to examine and compare the loss in value incurred when concept selection decisions are based on erred estimates of input variables. Errors can occur in estimates of expected values and in estimates of variance. The conclusion that erred estimates of input variables destroy project value can be made using common sense. What this study attempts to provide are original estimates of the potential magnitude of such losses, and an analysis of which input estimates matter more or less than others. In practice, one does not know if a current estimate for an input variable is erred, but one can estimate the impact of an alternate hypothesis being true, and this is the framework adopted here. A procedure for concept selection is defined to model the decision making process and is used in conjunction with a simplified asset development optimization model to estimate project values. The analysis compares project values resulting from concept selection decisions based on erred estimates and decisions based on an alternate hypothesis; in both cases, the alternate hypothesis is taken to be true. The difference in value observed, if any, is caused by sub-optimal initial facility capacity (note, the difference in value can also be interpreted as the maximum willingness to pay to confirm the alternate hypothesis). The approach is similar in form to standard VOI analyses.3–6
fax 01-972-952-9435. AbstractAn 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 take the form of seismic surveys, core and well test analyses, reservoir simulations, market analyses, price forecasts, etc., on which the industry spends billions of 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. Based on a survey of SPE publications, we provide an overview of the use of VOI in the oil and gas industry, with a focus on how the analysis was carried out and for which types of decisions VOI analysis has been performed. We highlight areas where 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.
Waterflood optimization via rate control is receiving increased interest because of rapid developments in the smart well completions and i-field technology. The use of inflow control valves (ICV) allows us to optimize the production/injection rates of various segments along the wellbore, thereby maximizing sweep efficiency and delaying water breakthrough. A major challenge for practical field implementation of this technology is dealing with geologic uncertainty. In practice, the reservoir geology is known only in a probabilistic sense; hence, the optimization of smart wells should be carried out in a stochastic framework to account for geologic uncertainty. We propose a practical and efficient approach for computing optimal injection and production rates accounting for geological uncertainty. The approach relies on equalizing arrival time of the waterfront at all producers using multiple geologic realizations. The main objective is to improve sweep efficiency and thereby improve oil production and recovery. We account for geologic uncertainty using two optimization schemes. The first one is to formulate the objective function in a stochastic form which relies on a combination of expected value and standard deviation combined with a risk attitude coefficient. The second one is to minimize the worst case scenario using a min-max problem formulation. The optimization is performed under operational and facility constraints using a sequential quadratic programming approach. A major advantage of our approach is the analytical computation of the gradient and Hessian of the objective function which makes it computationally efficient and suitable for large field cases. Multiple examples are presented to support the robustness and efficiency of the proposed optimization scheme. These include 2D synthetic examples for validation and a 3D field-scale application. The role of geologic uncertainty in the outcome of the optimization is demonstrated both during the early stage and also, later stages of waterflooding when substantial production history is available. Introduction The recent increase in oil demand worldwide combined with the decreasing number of new discoveries has underscored the need to efficiently produce existing oil fields. The maturity of most of the existing large fields requires prudent reservoir management and development strategies to maximize recovery. With this goal in mind, the use of smart/complex wells and completions are becoming increasingly common place. Among the various improved recovery schemes, waterflooding is by far the most widely used (Craig 1971; Lake et al., 1992). In spite of its many appealing characteristics, the presence of heterogeneity such as high permeability streaks might yield unfavorable results, causing premature breakthrough, poor sweep and consequently reduce oil production and recovery (Sudaryanto and Yortsos 2001; Brouwer and Jansen 2004; Alhuthali et al., 2007). Various methods have been suggested to mitigate this problem. Among these is smart well completion where the production or the injection section is divided into several intervals (Arenas and Dolle 2003; Glandt 2005). The flow rate at each interval can be independently controlled by inflow control valves (ICVs), making it possible to manage the flood front in highly heterogeneous reservoirs.
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