History Match of the Liaohe Oil Field SAGD Operation - A Vertical- Horizontal Well Reservoir Production Machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract With the decline of conventional oil production, developing and producing heavy oil resources efficiently is becoming more important. The Liaohe Heavy Oil Field steam operation is unique – it started with cyclic steam stimulation (CSS) operation that transitioned into a continuous steam-assisted gravity drainage (SAGD) operation. With respect to oil production in China, this field is considered critical for heavy oil production and technology development. Cyclic steam injection was initially done through vertical wells. This had the benefit that it provided a good start-up of depletion chambers in the reservoir. These chambers then grew under gravity drainage after continuous steam injection (through the vertical wells) and continuous production through a set of horizontal wells was started. Controlled and deliberate transition from CSS to a gravity drainage process with the objective of optimizing energy intensity (GJ injected per unit volume oil produced) with control enabled through production and thermocouple data is a smart field operation which we refer to as a Reservoir Production Machine (RPM). In this paper, as a first step to understand the operation and its impact on the reservoir, we have history matched the CSS operation based on the injection and production data from field. The use of vertical steam injection wells (formerly the CSS wells) in combination with horizontal production wells operated in a SAGD mode of operation is explored. The history-matched model can be used to develop automated RPM technologies to optimize not only energy intensity but also emissions intensity.
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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 | 0.000 | 0.000 |
| 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 it