Integrated Production Operation Models With Reservoir Simulation for Optimum Reservoir Management
Bibliographic record
Abstract
Abstract There has been an increasing interest in optimum reservoir management to maximize oil production from a reservoir given the present economic and technical limits. However, many reservoir management programs have failed because they do not consider wells, surface facilities and the reservoir as an integrated system. In fact, determination of optimum operation parameters for both producers and injectors is critical to the ultimate oil recovery under the existing reservoir and infrastructure conditions. On the other hand, production operation methods are usually not taken into account in reservoir simulation, which is traditionally used for reservoir management purpose. Also due to practical constraints, test runs are often limited to a few most plausible sets of model parameters, though the global optimum set should be used instead. This paper presents a systems engineering approach, which integrates production operation models with reservoir simulation to achieve the global optimum parameters for both producers and injectors. More specifically, a reservoir simulator starts with a basic run, which provides a reference case. Then the simulated annealing algorithm is employed to solve multi-dimensional nonlinear global optimization problems. Finally, the optimum reservoir performance is obtained by executing the reservoir simulator with the global optimum parameters. Such integrated approach ensures that the displacement front can advance slowly and steadily at various stages of the field development such that the maximum oil production can be achieved. It is also found that the simulated annealing algorithm is efficient and stable in achieving the global optimum. Furthermore, this systems method is applied fieldwide to a water-alternating-gas (WAG) miscible flooding reservoir. The reservoir performance has been adjusted and controlled in an optimum range over five years. The reservoir pressure remains higher than the minimum miscible pressure (MMP) and distributes uniformly in the whole reservoir, in which appropriate production operation methods are implemented. The bottom-hole flowing pressure, injection pressure, oil rate, injection volume of water and gas, water-cut and gas-oil-ratio (GOR) are kept in a proper range. All these fieldwide data show that the integrated approach to optimum reservoir management presented in this paper can greatly improve the oil production.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".