Increasing Operations Profitability Using an End-to-End, Wireless Internet, Gas Monitoring System
Bibliographic record
Abstract
Abstract Smart-Alek ™ is an example of a fully integrated, end-to-end gas measurement and production analysis system utilizing public wireless communications and a Web-browser-only delivery system to provide seamless well visibility to any desktop. Capitalizing on the technology's cost effectiveness and ease of deployment, Northrock Resources Ltd. (a wholly-owned subsidiary of Unocal Corporation) has adopted a grass roots shift in their operations strategy that implements on-the-ground, timely decision- making at the field level. This technology fits Northrock's operations business needs of easy to use, complete, reliable, and cost effective production information delivered in a timely manner to everyone on the team. Previously, Northrock has found that these needs were not adequately met by conventional SCADA technologies. This type of end-to-end wireless and Web technology has enabled Northrock to increase gas volumes with more accurate measurement (Electronic Flow Measurement vs. mechanical chart), and less downtime. Reductions in operating osts were achieved by decreases in well visitation frequency and a redirection of operator "windshield time" to activities that Introduction Although advances in SCADA (Supervisory Control And Data Acquisition) have improved gas measurement technology from the 100-year-old mechanical chart methods, costs can be prohibitive to implement an electronic alternative. The following case study investigates how Northrock increased profitability deploying and utilizing Smart-Alek ™ company-wide while avoiding these high mplementation costs. Smart-Alek ™ is an example of a new type of end-to-end electronic Gas Flow Measurement (GFM) system, entitled FINE ™. FINE ™ is an acronym meaning Field Intelligence (FI), Network (N), and End-User Interface (E)(1, 2). Background A comparison of GFM information flow between mechanical charts, SCADA, and FINE ™ is given in Figure 1. On the left of this figure is the information flow path of the mechanical chart method. Data gathering using mechanical charts, typically involves daily site visitations by an operator who estimates the previous day's volume from chart readings. This rough estimate is then manually entered into a Field Data Capture system (FDC). Once captured, these rough production volumes are then accessible by operations staff, engineers, and management who use them to make daily business decisions. Use of the rough volumes in FDC and integrated chart volumes for accounting is error prone and time intensive, and it introduces significant business challenges in the flow of information from the wellhead to the producers bank account. The business impacts of these inefficiencies and errors are as follows:Obtaining on-site daily estimates requires significant operator time.Both on-site estimating and chart integration can be highly inaccurate because of the numerous manual steps required by many people.Accuracy of integrated volumes is highly dependent on fixed meter run parameters and gas composition. These parameters are error prone because they are also stored manually in multiple systems (i.e., log books, spreadsheets, meter detail, and chart integration files), which translates into increased rework costs and potential lost revenue.Chart integrated volumes are typically not reconciled with field estimates. This can result in undetected errors that have a direct impact on cash flow, which can be large and long term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".