For CO2 enhanced oil recovery (CO2-EOR) and CO2 storage projects, cost estimates have wide-ranging impacts on site screening and feasibility studies, concept selection, development planning, and production optimization. Good cost estimating practice requires technical knowledge about the project, a comprehensive scope, and a systematic approach. However, the quality of a cost estimate is probably most dependent on the quality of the data on which it is based. In the case of CO2-EOR and CO2 storage projects (CO2 projects), cost estimators face significant challenges regarding the availability and quality of data. The purpose of this paper is to organize and consolidate information on capital and operational costs for CO2 projects. A review was undertaken to identify and describe sources for cost data by major project components. There are sections for drilling, surface facility capital cost, and operation and maintenance cost. This survey can serve as a departure point for CO2 project cost estimators.
Summary This study examines the loss in project value incurred when concept selection decisions are based on erroneous estimates of input variables. Estimates of the magnitude of such losses are provided, along with an analysis of which input variable 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 erroneous estimates to values based on an alternate hypothesis. Results suggest that the cost of using erroneous estimates for initial costs, expansion costs, and the timing of future expansion projects is comparable in magnitude to the cost of erroneous 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 (Evans 2005; Walkup and Ligon 2006). 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 erroneous estimates of input variables. Errors can occur in estimates of expected values and in estimates of variance. The conclusion that erroneous estimates of input variables can destroy project value is common sense. What this study attempts to provide are original estimates of the potential magnitude of such losses, and an analysis of which input variable estimates matter more than others. In practice, one does not know if a current estimate for an input variable is erroneous, 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 erroneous 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 that 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 (Coopersmith et al. 2006; Bickel et al. 2006; Prange et al. 2006; Gilbert et al. 2007). The number of input variables normally required to estimate the value of competing development concepts is immense, and estimates are required from all project disciplines. This study examines the loss in value associated with errors in estimates of three key input variables: reserves, initial facility cost, and facility expansion cost and schedule. These three variables have a large impact on project value. The reserves estimate drives most major development decisions, including the depletion plan, well counts, and facility design. Facility cost estimates influence concept type, initial facility capacity, and plans for future expansion. Estimates for the cost and timing of future facility expansion affect the initial facility capacity decision and the value that can be captured given upside realizations of subsurface scenarios. The results of this study provide insight on the magnitude of losses associated with erroneous estimates for these input variables, and enable a relative ranking of these inputs based on the impact on project value.
Allocating production volumes across a portfolio of producing assets is a complex optimization problem. Each producing asset possesses different technical attributes (e.g. crude type), facility constraints, and costs; In addition to these field-level specifications, there are corporate objectives and constraints (e.g. contract delivery requirements). While complex, such a problem can be specified and solved using conventional deterministic optimization methods. However, there is often uncertainty in many of the inputs, and in these cases the appropriate approach is neither obvious nor straightforward. One of the major uncertainties is the commodity price assumption(s). This paper tackles this problem in three major sections: (1) We specify an integrated stochastic optimization model that solves for the optimal production allocation for a portfolio of producing assets when there is uncertainty in commodity prices, (2) We then compare the solutions that result when different price models are used, and (3) We perform a value of information analysis to estimate the value of more accurate price models. Price modeling can affect decision-making, but it is surprising to find so little research that relates the price modeling assumptions (model type and the respective parameters) to the decisions that result from their use (capital investment, production optimization, etc.). Instead, we observe countless papers advocating for more and more complex price models, and an equally large body of work where models are estimated and compared for their accuracy. No one appears to have asked the basic question, "Do any of these models provide any incremental value for decision-making?" Here, we address this question by specifying an integrated stochastic optimization model that simulates decision-maker behavior. Using this model, we compare and contrast the various production allocation decisions that result from different price models. Simple price models are investigated and compared to several currently popular advanced price models with the goal of understanding the impact of price model assumptions (both the model type and its parameters) on decision-making. The results show that the optimum production allocation is a function of the price model assumptions. However, the differences between models are minor, and thus the value of choosing the "correct" price model, or similarly of estimating a more accurate model, is small. This work falls in the emerging research area of decision-oriented assessments of information utility/value. We believe it to be the first paper of its kind on this subject in the upstream literature.
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