Wells and Facilities Instrumentation and Automation Towards Achieving Field Intelligence
Bibliographic record
Abstract
Abstract A well is a conduit that connects the hydrocarbon deposits in the subsurface to the facilities that transfers and processes it. Understanding the flow of oil or gas through the source rock can only be made possible from the wells itself. Aside from the flow conduits, wells are the only point of reference to understand the field wide behavior. This information is critical to manage the reservoir, ensuring sustainable production throughout the field life. Traditionally, acquiring data from the subsurface to the wellhead relies on intervention, conveying instruments downhole with wireline or coiled tubing. Though effective, this activity incurs costs, logistically challenging and only sporadically available. Surface flow parameters such as rate and pressure are usually measured by analog gauges and Barton chart measurements, which are read manually by personnel to be tabulated later. In most cases, these data can be lost without a proper data management system in place. With the advent of digital instruments, parameters such as pressure, temperature and flowrates; can now be measured automatically and transmitted to a DCS or SCADA system. Some downhole completions are now equipped with instruments that are robust and accurate to take measurements even in extreme conditions of heat and pressure. With data at high availability, engineers are now able to conduct analysis faster, applying data analytics, collaboration and decision making. The main value for Digital Oilfield (DOF) is to save time in data retrieval, analysis and decision making and allow domain engineers to perform higher analytical function and decision making, taking them out from repetitive, manual work through automation. This paper will describe the minimum instrumentations for all well types and major oil and as process facilities for real time data acquisition required to run DOF workflows. This covers subsurface wellbore to production manifold to custody transfer meters.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".