Oilfield Build Own Operate BOO Projects - A Good Execution Strategy for the Current Low Oil Price Environment
Bibliographic record
Abstract
Abstract Objectives/Scope In the current low oil price environment, oilfield Build Own Operate (BOO) and Build Own Operate Transfer (BOOT) projects deserve consideration. Although generally the objective of BOO and BOOT projects are to reduce upfront capital costs and transfer risk to the BOO contractor, BOO and BOOT projects also offer excellent opportunities to incentivize innovation and reduce project lead time to complete, lowering project life-cycle costs and accelerating project returns. If the BOO project is structured properly - safety, quality, and reliability are not sacrificed. Methods, Procedures, Process This paper compares and contrasts BOO and EPC projects to describe advantages and tradeoffs for successful application of BOO projects. The performance of the Kuwait Oil Company (KOC) EPF-120 project in North Kuwait is reviewed, as a successful example of the application of BOO projects in the oilfield. The EPF-120 project is a BOO grass roots centralized Early Production Facility that processes 120,000 BPD of crude oil, 84,000 MMSCFD of gas and 80,000 BPD of produced water. The project has now been in service for the full term of its five years operations contract. Results, Observations, Conclusions The typical structure of BOO projects and EPC projects are described, and conventional EPC projects are compared to BOO projects. Incentives and disincentives for innovation are discussed. Typical project timeline for EPC projects will be compared to BOO project timelines. Mechanisms to achieve quality, safety, and reliability are provided. As an example of the opportunities that BOO projects offers, operations, maintenance, environmental, and safety performance of the EPF 120 project is reviewed, and examples of innovation implemented for the EPF 120 and for other BOO projects are provided. Additional costs of BOO projects - namely risk and financing are provided and compared to potential reduced life cycle cost and reduced project completion timeline. Operating challenges to execute a BOO project in an existing oilfield are presented. Trade-off between flexibility/innovation and specification/standardization are also discussed. Novel/Additive Information BOO is a solution in the oilfield to provide innovation and improved schedule without sacrificing quality, safety, and reliability. The skills required to successfully execute a BOO project will be detailed and include the following: process engineering and Front End Engineering Development (FEED) skills, capital and operating cost estimating capability, Engineering, Procurement, and Construction Management (EPCM)
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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 | 0.000 | 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 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".