Benchmarking Of Heavy Oil Fields: A Tool for Identification of Opportunities for Total Cost and Production Optimization
Bibliographic record
Abstract
Abstract This paper presents a practical method for benchmarking heavy oil fields as a tool for identification of opportunities for total cost and production optimization. The method combines actual data from typical heavy oil fields to define reservoir, well and surface complexity indices, for categorizing a subject field and a total cost breakdown model to map potential risks that could cause total cost to increase, potential project/process delay and poor production performance. The benchmarking process consists of four steps: 1) classification of a subject field using Front End Loading (FEL) and complexity indices that account for: a) reservoir structural, stratigraphic, rock, fluid, energy, static and dynamic complexity, b) well complexity and c) surface processes complexity; 2) selection of analog fields within the range of indices; 3) use of causal maps to identify causes of uncertainty and risks that impact capital expenditures (CAPEX), operational expenditures (OPEX), production losses and cycle time; and 4) a total cost stochastic model is used to generate graphs providing the position of the subject field vs. analogs. A total undiscounted cost breakdown structure provided information on the most critical cost drivers, where significant impact corresponded to OPEX. Causal maps described typical total cost drivers for surface and subsurface. Seven most significant groups of risks are modeled to visualize the impact on cost, production losses, cycle time and health, safety and environment with recommended mitigation actions ranked by cost benefit. A database provides information about cost of production (Capex, Opex) from heavy oil fields undergoing cold production and thermal enhanced oil Recovery well-known heavy oil production areas from Venezuela, Canada, USA and Middle East. Heavy oil fields undergoing thermal enhanced oil recovery indicated typical ranges for Opex from 2 to 22 USD/bbl and Total Cost ranges from 10 to a maximum of 40 $/bbl. A key observation is that cost of fuel and power is the largest single OPEX cost for thermal enhanced recovery accounting for about 50%. Significant production losses are associated to failures due to corrosion and blowouts is the most significant HSE risk. The proposed method helps benchmarking total costs in heavy oil fields, which is a task that requires lot of efforts in researching available reliable sources from technical papers, regulatory agencies, and oil industry. Understanding causes of high cost per barrel and their relationship with uncertainties and risks for heavy oil field, is a formidable tool for multidisciplinary cost optimization as it provides a common language that understood by all disciplines involved.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".