Waterflooding Optimization under Geological Uncertainties by Using Deep Reinforcement Learning Algorithms
Bibliographic record
Abstract
Abstract Recently, substantial technical progress has been made to solve complex tasks in the field of artificial intelligence (AI) by incorporating deep neural networks into reinforcement learning (RL). In this paper, four state-of-the-art deep RL algorithms are applied to optimize the net present value (NPV) of waterflooding (WF) under geological uncertainties by adjusting the water injection rate. They include the deep Q-network (DQN), double DQN (DDQN), dueling DDQN, and deep deterministic policy gradient (DDPG). A set of fifty reservoir realizations are generated by using a geostatistical technique to account for the geological uncertainties. It is found that the deep RL algorithms can optimize the WF in a 3-D 3-phase (oil-water-gas) reservoir under geological uncertainties. More specifically, both DQN and particle swarm optimization (PSO) converge to the same highest NPV, whereas the other three deep RL algorithms can find some local optimum NPVs due to the exploration-exploitation problem. DDPG converges faster than PSO and requires the least numerical simulation runs among all deep RL algorithms. The optimum water injection rate determined in the consideration of geological uncertainties not only increases the expected NPV but also reduces its standard deviation. The optimum WF starting time is found to be in the middle of the primary production. In this way, the solution-gas drive is continued and the water-cut is decreased. The production performances are compared under three different water injection scenarios: no-control, reactive-control, and optimum-control. The optimum-control scenario achieves a low water-cut and a stable oil production rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".