Real Time Optimization of a Water Flood Reservoir
Bibliographic record
Abstract
Abstract Due to the rising rate of demand for hydrocarbon fuels as well as descending trends in reserve discovery it is of increasing necessity to produce oil and gas fields more efficiently and economically. In this scenario, the use of secondary recovery processes, i.e. waterflooding, is becoming more and more imperative. Also the use of smart wells, which are equipped with data acquisition, monitoring and controlling instruments, is also being proposed as efficient means of maximizing ultimate recovery. In this work a methodology to optimize petroleum production or net present value (NPV) from a waterflood reservoir by controlling operational parameters of the production and injection wells with the use of smart well technology is developed. The optimization procedure involves maximizing the objective function expressed in terms of cumulative oil produced or NPV from a waterflood reservoir by adjusting a set of operational parameters expressed as the production wells flow rates and injector wells bottomhole pressure. The production and injector wells in this reservoir are smart wells whose downhole chokes are automatically adjusted to meet certain optimal requirements. Production strategies can be derived from numerical optimization analyses, the solutions for which are most frequently based on optimal control theory. The approach used in this work considers a simpler alternative to the optimal control approach: a moving-horizon formulation coupled to a heuristic algorithm. This work also provides a validation of the optimization methodology proposed through two heterogeneous and one homogeneous reservoir models with five-spot pattern waterflood schemes. The results of the optimization methodology proposed in this work show an increase in cumulative production and in NPV with respect to the base case. Introduction The increasing availability of intelligent and smart wells as well as integrated technologies permit practical acquisition of production information from every well and dissemination for use by everyone within an organization and allow overall field optimization of operations in real time to happen. Instrumented wells with real-time downhole measurements and remotely activated valves, public wireless communications, database and interface tools provide key and powerful developments for reservoir management and production optimization to gas and oil producers. The concept of real-time optimization (RTO) is not new and has been practiced in some areas of Petroleum engineering like drilling or production operations for quite same time but due to new technologies related to data measurement and processing the extent to which RTO is now feasible has increased tremendously. A well-established definition for RTO is that "it is a process of measure-calculate-control cycle at a frequency, which maintains the system's optimal operating conditions within the time-constant constraints of the system". Based on this definition it is possible to conclude that the concept of realtime optimization requires the use of measurements of a process at a certain frequency to enable decision-making at the same frequency. This whole procedure would ideally be performed continuously, by collecting all available data in real time and also continuously solving all-encompassing optimization problems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".