Intelligent Production Optimization Decisions: Prioritizing Production Optimization Through Machine Learning Aided Uplift Quantification
Bibliographic record
Abstract
Abstract Due to complexity and scale, managing and engineering a modern hydrocarbon asset is extremely challenging. The problem becomes more difficult if an asset has thousands of wells to manage because, most of the time, operation resource is limited (either manpower or tool) to achieve production optimization and the amount of information to process during decision making is enormous. Bringing necessary data from multiple different sources and analyzing it in a relatively short period of time to provide actionable/reliable insight to field operators’ and engineers’ is the key to enhance and optimize production performance. The production optimization best practice is shared amongst field operators and engineers given the geology, reservoir character, and operation strategy which include artificial lift, stimulation, and other workover. This approach has been applied and proven successful mostly in low well count assets by identifying opportunity and providing robust qualitative guidance towards optimum production condition. However, in high well count assets such as unconventional, it is impossible to execute traditional manual approach and it is essential to develop an integrated, flexible, and automated workflow that enhances the efficiency and effectiveness of production operation and achieve "operate-by-priority". Thus, to have efficient and optimized management of hydrocarbon production from individual well of a high well count asset requires prior, more certain and almost real time knowledge of production rates from the currently or upcoming production wells. Production data, thus, from individual well is very important. Well tests are carried out to obtain production rates and then utilize these rates to provide optimized production decisions and actions. In a well test, a test separator splits the production stream into oil, gas, and water using gravity and support in the management of hydrocarbon production. However, well test are infrequent and doesn't fit the purpose alone with such low frequency for real time surveillance and decision support. Also, well tests are expensive and, hence, it is also not economically viable to do more frequent well tests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".