How Could You Possibly Predict the Value of 3-D Seismic Before You Shoot It?

Abstract

Abstract It is relatively easy to estimate the cost of acquiring, processing and interpreting 3-D seismic data, but what about predicting its value? Some prediction is needed in order to make a business case for acquiring the additional data. The Value of Information (VOI) can be estimated by combining a number of financial and statistical analytical methods. The VOI will ultimately depend on two key components: the possible impact on the amounts and timing of expenditures and revenues, and on the resulting Net Present Value (NPV) of the anticipated cash flow stream; andthe possible impact on the Probability of Scientific Success (Ps). Published historical information from several companies shows that 3-D data has reduced structural uncertainty, increased reserves by identifying by-passed pay zones, improved drilling success rates, reduced the number of development wells required through optimum well positioning, led to faster development programs and faster pay-out, and terminated numerous prospects. The impact of these effects can be estimated based on industry experience. This leads to evaluating how the additional data might impact the drilling and capital expenditure program. This could range anywhere from condemning the prospect to drilling a significantly larger number of wells and building proportionately larger gathering and processing facilities, which in turn could delay revenues and expenditures. When the realm of possibility is understood, the potential effect on the Net Present Value of the anticipated cash flow stream can be estimated. Understanding the potential impact on the cash flow stream makes it possible to determine the Expected Value of Perfect Information (EVPI). Since no information is perfect, the EVPI will represent an upper limit on the VOI. Residual uncertainty will determine the actual value of the additional information. This leaves the possible effect on the Probability of Scientific Success. There are a number of methods for estimating this effect including Delphi methods, Monte Carlo simulation, and using published industry data. Regardless of which method is used, internal consistency will be critical to producing a reliable estimate. Once the possible effects on NPV and on the Probability of Scientific Success are known, the Value of Information can be predicted using a Decision Tree method. It then remains to compare the value of information with the cost to determine whether to proceed.