Optimization of Direction and Length of Horizontal Wells in Oil Field-X Using Fuzzy Substractive Clustering and Fuzzy Logic Methods
Bibliographic record
Abstract
This study discusses an optimization model to obtain the optimal direction and length of horizontal wells in the oil field X. In the common practice in oil industries, the optimal direction and length are obtained from a trial and error method through a considerably time consuming reservoir simulation runs. Employing the basic reservoir properties data of the porosity, permeability, oil saturation, and location of each grid of the available reservoir model, Fuzzy Subtractive Clustering is used to classify grids. Furthermore, Fuzzy Logic optimization model is built to find the optimal direction and length of the horizontal well. Determination of the optimal direction and length is based on the oil recovery represented by a recovery factor that does not have any simple relationship with the basic reservoir properties data, but, through a reservoir simulation model that basically calculates fluid flows in porous media performances using a finite different formulation of the governing equation, i.e., the diffusivity equation. The filed case discussed in this study is one with the drilling starting point already known. The results of the study show that the methods of Fuzzy Subtractive Clustering and Fuzzy Logic are effective in determining the optimal direction and length simultaneously without running the reservoir simulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".