Automated Drillstem Test Interpretation Using Nonlinear Regression Methods
Abstract/Contents
- Abstract
- A Drillstem Test (DST) is an important test because the results, combined with geological and other reservoir data, are the basis for deciding the commerciality of a discovered hydrocarbon accumulation in a reservoir. However, a DST is usually conducted as quick as possible because drilling rig cost is expensive. Therefore, the amount of reservoir fluids and the data obtained during the test may be limited. Furthermore in some cases, the physical behavior which occurs during a DST does not satisfy the assumptions used by the most commonly available interpretation techniques. This study concerns DST's that produce liquid, and the flow does not reach the surface. When the well is opened, the initial flow rate is high because the wellbore pressure drops from an initial reservoir pressure to a level that is close to atmospheric pressure. The reservoir liquid flows into the wellbore and accumulates in the test string, and therefore the liquid level rises. The rising liquid level increases the wellbore pressure and reduces the flow rate. In some cases, the flow stops even before the well is shut in. These conditions are similar to slug tests conducted in water wells. The mathematical solution for this problem was presented by Agarwal and Ramey in 1972.A computer-aided method was proposed in this study to interpret DST data wherein the flow does not reach the surface. The computer programs were developed using the Gauss-Marquardt nonlinear regression method, which has been proven a most reliable algorithm for well test interpretation. The results were compared with graphical analysis.
Description
| Type of resource | text |
|---|---|
| Date created | April 1993 |
Creators/Contributors
| Author | Harsono, Djoko |
|---|---|
| Primary advisor | Ramey Jr., Henry J. |
| Degree granting institution | Stanford University, Department of Petroleum Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
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Preferred citation
- Preferred Citation
- Harsono, Djoko. (1993). Automated Drillstem Test Interpretation Using Nonlinear Regression Methods. Stanford Digital Repository. Available at: https://purl.stanford.edu/kv867xr2894
Collection
Master's Theses, Doerr School of Sustainability
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