Determination of Production Operation Methods in Naturally Fractured Reservoirs
Bibliographic record
Abstract
Abstract There are many naturally fractured reservoirs in the world, but few of them are optimally developed. In fact, it is difficult to characterize the naturally fractured reservoirs and predict the oil production, needless to mention the determination of appropriate production operation methods (POMs). Although there have been some formulas for evaluating well performance, a few were derived on the basis of production test data. In this paper, several general formulas are developed for evaluating inflow performance of both vertical wells and horizontal wells, based on the production test data obtained from three naturally fractured reservoirs. The influence of rock compaction and the inertial flow resistance in naturally fractured reservoirs are considered in these equations. Furthermore, theoretical models are also presented, into which reservoir engineering, production performance and surface facility performance are incorporated. These models are then applied to evaluate and determine the oil well POMs for two naturally fractured reservoirs. It has been shown from these two field applications that stable flowing performance, including its ceasing conditions, can be predicted. And artificial lift methods such as sucker-rod pumping can be efficient under certain reservoir conditions. The detailed field application results indicate that most of POMs determined from the theoretical models are technically feasible and economically viable. Introduction Naturally fractured reservoirs are found in all types of lithologies and throughout the geological stratigraphic columns. However, initial high oil rates have misled engineers in many instances to overestimate production forecasts of wells. Thus development of the naturally fractured reservoirs results in numerous economic failures(1). Meanwhile, field practices show that selection of appropriate production operation methods (POMs) is critical to the long-term profitability of most producing wells(2–8). An improper choice can not only substantially reduce production but also greatly increase operating costs. Once a type of POM has been determined to install on a producing well, usually the POM is unchanged, whether it was and still is the optimal choice under existing conditions. Therefore, It is essential that both accurate prediction of well inflow performance and appropriate selection of POMs be of great benefit to the optimal development of the naturally fractured reservoirs. In general, it is difficult to characterize the naturally fractured reservoirs, predict the oil production and further determine suitable POMs. The well inflow performance relationship (IPR), which represents the well's ability to produce fluids, is the first component to be considered in the process of selecting POMs(9). In the literature, although there have been some formulas for evaluating well performance, few were derived on the basis of production test data. Gubkina(10) presented a formula for evaluating vertical well inflow performance in the naturally fractured reservoirs, which was later improved by Bacnev et al.(11). However, the effect of well completeness on well inflow performance was not accounted for. To evaluate the horizontal well inflow performance in naturally fractured reservoirs, Joshi's formula(12) is modified to achieve better forecasts(13,14).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, not a consensus.
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