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 developed optimally. In fact, it is difficult to characterize naturally fractured reservoirs and predict their oil production, let alone determine their appropriate production operation methods (POMs). Although there exist some formulas for evaluating well performance, few are derived on the basis of production test data. In this paper, several general formulas are developed to evaluate inflow performance of both vertical and horizontal wells, based on the production test data of three naturally fractured reservoirs. The rock compaction and the inertialflow resistance in the naturally fractured reservoirs are taken into account in these equations. Furthermore, theoretical models are presented to consider reservoir engineering, production performance, and surface facility performance. These models are then applied to evaluate and determine the POMs for two naturally fractured reservoirs. These two field applications show that stable flowing performance can be predicted accurately, and that artificial lift methods, such as sucker-rod pumps, can be mployed efficiently under certain reservoir conditions. The detailed field application results indicate that most of the POMs, as suggested by 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, the initial high oil rates seen in these reservoirs have misled petroleum engineers, in many instances, to overestimate their future production performance. Thus, the development of naturally fractured reservoirs has resulted in numerous economic failures(1). Meanwhile, field practices show that the 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 oil production, but also greatly increase operating costs. Once a POM is employed in a producing well, usually this POM remains unchanged, regardless of whether it will still be the optimal choice under subsequent conditions. Therefore, both the accurate prediction of well inflow performance and the appropriate selection of POMs are of great benefit to the optimal development of naturally fractured reservoirs. In general, it is difficult to characterize naturally fractured reservoirs, predict their oil production, and further determine suitable POMs. The well inflow performance relationship (IPR), which represents the well deliverability to produce fluids, is the first component to be considered in the process of selecting POMs(9). In the literature, although there are some formulas available for evaluating well performance, few are derived on the basis of production test data. Gubkina(10) presented a formula for evaluating the vertical well inflow performance in naturally fractured reservoirs, which was later improved by Bacnev et al.(11) However, the effect of well completion on well inflow performance was neglected. Joshi(12) and Karcher et al.(13) developed models to evaluate the horizontal well inflow performance in naturally fractured reservoirs. Mullane et al.(14) improved Joshi's method to achieve better forecasts. Other formulas(15–17) were developed, in which several unknown quantities are difficult to obtain from oil fields.
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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 | 0.004 | 0.001 |
| 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.
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".