Evaluating Reservoir Aspects From Database on Infill/Step-Out Wells
Bibliographic record
Abstract
Abstract About 200 000 infill/step-out wells have been drilled in the Western Canadian Sedimentary Basin (WCSB) since 1980. This wealth of data implicitly included clues to various reserve characteristics but techniques for evaluation were not obvious to us. We tried different ways of processing data for deriving meaningful insights. In this paper we present some of these with appropriate examples. Pools were grouped by major formation type, fluid type, and depletion mechanism. The distribution performances of various sub-groups were individually evaluated. Where possible, performances of horizontal and vertical wells were treated separately. We also compared the value of the Average and the Median performance parameters. In most cases the average was much larger than the median. We prepared frequency plots and their representation on the normal (standard deviation) scale for the wells in each group. From these plots we were able to characterize the lateral heterogeneity of the reservoirs. This could be used in applying risk factors to future projects. Risk analysis can also be aided by other features of frequency distributions for corresponding wells. A rate-cumulative production plot was also helpful in determining acceleration benefits and incremental reserves resulting from infill drilling. Introduction Two hundred thousand infill and step out wells were drilled in the WCSB between 1980 and 2005 (Table 1a). Infill wells for oil and gas were approximately equal in numbers. About 75 percent of the wells were drilled in sandstone formations and 25 percent in carbonate formations. Overall, 50 000 wells were drilled in sandstone heavy oil reservoirs and 80 000 in sandstone gas reservoirs. Of these, more than 50% were shallower than 500 meters. In this paper we focused mainly on the performance of infill wells in mature light oil carbonate reservoirs. First, we reviewed the performance of two Mississippian pools in SE Saskatchewan (Williston Basin). Here, the horizontal wells performed noticeably better than the contemporary vertical infill wells. In Alberta however, in the three carbonate pools reviewed, two oil pools and one gas pool, the vertical infill wells performed better than the horizontal wells. (In the case of the Swan Hills BHL pool the difference was small) We also reviewed the performance of a heavy oil sandstone reservoir and a shallow sandstone gas reservoir. In both pools, the horizontal infill wells performed better than the vertical wells. A major objective of this paper is to show how a relatively simple statistical analysis of the performance curves could provide useful insights; and also, to present a method to qualitatively/quantitatively characterize the lateral heterogeneity as well as, risks in developing similar pools by infill drilling. In addition, we also illustrate how the oil rate-cumulative production plot could be used to identify contributions due to acceleration and incremental recovery. We have incorporated some of the ideas discussed in previous papers2–8. METHODOLOGY Our main data sources were the provincial Oil and Gas Reserves Books. Production data were obtained from a commercial data base. An "in-house" Excel based statistical program was used for analyzing/sorting the data and developing various statistical parameters, tables and graphs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".