Information is Costly, but How Valuable is It?

Field: Energy (Physical Sciences)

Information is Costly, but How Valuable is It? Hector A. Wills; Hector A. Wills Petroleos de Venezuela Search for other works by this author on: This Site Google Scholar Ramona M. Graves Ramona M. Graves Colorado School of Mines Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, September 2004. Paper Number: SPE-90710-MS https://doi.org/10.2118/90710-MS Published: September 26 2004 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Wills, Hector A., and Ramona M. Graves. "Information is Costly, but How Valuable is It?." Paper presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, September 2004. doi: https://doi.org/10.2118/90710-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractThis paper presents a method for economic resource allocation for additional data acquisition using the value of information concept. To apply it, the basic condition is that at decision time the available information does not describe conclusively the state of nature. Consequently, the new information might lead to a change in the decision to be made because it has the ability to modify positively the prior perception about the state of nature. The maximum value of the new information comes from how well this perception, in probabilistic terms, is modified from its original value towards the probability of conclusive or perfect information value. The new information value is calculated before acquiring it; using Bayesian theory, with decision tree methodology in conjunction with Monte Carlo simulation applied to the estimated ultimate recovery (EUR) algorithm. A sensitivity analysis on the Bayes' conditional probabilty term is performed, and the results are presented in graphic form as the information surface, its 2-D projection and the Information index plot.The model was built on spreadsheet based commercial software.IntroductionPrevious work in estimating value of information in the petroleum industry has been deciding the use of a particular application such performing a 4-D seismic study, running a porosity log, drilling an appraisal well, etc. The underlying principle is the comparison between the possible economic worth with information and the possible economic worth without information. To complete the analysis one may ask Where should the economic resources be placed when several information sources are available? How does this economic worth comparison behave when it is evaluated in reserves estimates context? How about evaluating in terms of the information's usage? This study(1) was originated to find an answer to these questions.We chose focusing on estimated ultimate recovery (EUR) as a working algorithm because strategic decisions in the petroleum industry are based principally on estimated reserves. However, almost every reserve-related decision is made under uncertainty because conclusive information about the reservoir is unavailable at the decision time. It is universally accepted that the uncertainty level of an estimate is related to the amount and quality of data, and hence acquisition of more data should reduce the uncertainty(2). This statement is based on Arps' work(3) on reserve estimation and uncertainty tracking.It is less universally accepted that information does have a cost and it does or does not have a value. Costs are generally known before gathering information, i.e. from contracts, price lists, and direct negotiation with service companies. Value, before gathering it is going to be calculated using this method(4).Value of InformationIn general, as several authors extensively report it, value of information calculation is performed using the following equation:Equation 1The expected monetary value (EMV) calculation is used because there is uncertainty affecting some of the variables in a given model and the outcome can be described by a cumulative probability distribution.In a petroleum reserves context, the general uncertainty is a composite of the variables' particular uncertainty. An example is calculating estimated ultimate recovery (EUR) using equation (2). The techniques used so far target the identification of the most important variables and then valuing the information for those variables(5).Equation 2The methodology developed in this study, goes one step further in assigning value to the variables involved in equation (2), by considering several information sources' probability of giving conclusive information regarding the variable being measured and the effect on the EUR through equation (2). For example, the probability of the lithodensity log determining the porosity in the well logged is estimated to be 90%; the effect on the correspondent economic evaluation is reflected in equation (1) in the EMVnew inf term. Keywords: water saturation, information, reservoir characterization, recovery factor, conclusive interpretation, conclusive information, sensitivity analysis, probability distribution, artificial intelligence, upstream oil & gas Subjects: Reservoir Characterization, Formation Evaluation & Management, Risk Management and Decision-Making, Information Management and Systems, Open hole/cased hole log analysis, Decision-making processes This content is only available via PDF. 2004. Society of Petroleum Engineers You can access this article if you purchase or spend a download.