Two Drill stem tests (DST) were conducted from a well X, in an offshore field that was in the exploration phase. A major objective of each test was to identify whether the seismic anomalies seen between well X and a bounding fault act as impediments to flow. This objective was robustly achieved, together with all other test objectives, without unnecessary expenditure of rig time.
An expert had planned the tests using analytic models and his considerable experience. Pressure/rate deconvolution was used to support the interpretation. Since the tests were performed, a new method for DST design has been (recently) developed. It uses Experimental Design to enable systematic calculation of the expected chance of success of a DST when several scenarios exist for the reservoir. The method also provides a way to maximize these chances, given fixed resources, such as rig time.
The new method was tested against the actual plans used for the DST's in well X, and indicated that they were indeed optimal.
Experimental Design is rapidly gaining industry acceptance as a method to assess risks in field developments that arise due to reservoir uncertainty. But, its application to DST design when significant reservoir uncertainty exists, has rarely (if ever) been reported. The new method may exploit analytic models and/or finite difference simulation (coupled with geological modeling packages) to predict DST behavior. It is efficient and may be employed to rapidly redesign DST programs whenever new data becomes available, for example after open-hole logs have been acquired in a well to be tested. The cases described here illustrate why Pressure/rate deconvolution shows much promise to improve pressure transient interpretation.
Introduction
In this paper we first report on the interpretations of two DST's that were performed in an offshore field undergoing exploration. Rate deconvolution was used to generate the single rate system response, and consequently, to help provide the following key benefits:a significant reduction in the ambiguities associated with the interpretations of conventional derivatives of the pressure buildups, andenable a characterization deeper into the reservoir than that possible with these derivatives. Readers who wish to know about rate deconvolution may refer to any of [1],[2],[3].
Then one of the DST cases is used to demonstrate a new method of job planning that is based upon Experimental Design followed by Monte Carlo simulation. This method is suitable when little is known about the characteristics of a field that is in an exploration or early appraisal stage, since a major challenge when designing a DST program to be performed in such a case is to be confident that the program meets the objectives that have been defined for it - no matter the reservoir or well completion conditions that are actually encountered.
In these circumstances, a logical step in the design of a DST program would be to screen it for success, and thereby ensure that it works (i.e. meets the objectives) across a acceptably high proportion Psuccess, of the significant reservoir scenarios that could be conceivably encountered - provided that this step can be executed in an operational environment with sufficient speed. Another way of viewing Psuccess is to treat it as the probability that the test program will be successful, no matter the actual reservoir and completion conditions that may be encountered. Experimental design (ED) coupled with reservoir modeling is an industry accepted method to scientifically yet efficiently screen a reservoir response (here, the reaction of the reservoir to the DST) when there is significant uncertainty about the reservoir and potential operating conditions.
