Evolution of a Wells Decision Support Center as a Hub for Operational Excellence
Bibliographic record
Abstract
Summary Chevron's Wells Decision Support Center (DSC) has been evolving since it was founded in 2011. Originally called the Real-Time Drilling and Optimization Center (RDOC), it was later renamed to more closely reflect the work and the advisory nature of the services provided. The original DSC (or RDOC) was established after the Macondo incident in the Gulf of Mexico. Its primary focus was managing process safety risk—well control in particular—in deepwater and complex wells as defined by Chevron's global standard operating procedures. The role of the DSC in process safety is primarily in an advisory capacity while the decision making is the responsibility of the operations team on the rig and in the business unit. The original DSC was established as a partnership between Chevron and a data aggregation and visualization service company. The visualization software tools enabled the DSC's experienced engineers and Drill Site Representatives (DSRs) to monitor operations on a 24-hour basis. A separate team of experienced engineers provided analytical support, and a team of IT professionals provided the foundational IT support to the 24/7 team. This combination of experienced operations, engineering, and technical support professionals facilitated communication and built credibility with business units, which made the Wells DSC an integral part of Wells operations around the world. As shale and tight rock plays evolved in North America, and later expanded internationally, it became a significant piece of Chevron's business. Today, there are unconventional operations in the U.S., Canada, and Latin America. Process safety is very important in all plays, but cycle time and costs are also important business drivers for shale plays, so the DSC expanded its scope. This was done by developing new workflows, leveraging digital tools, and collaborating with geology and geophysics (G&G) teams. The DSC integrated further by adding a geosteering team for unconventional resources in 2018. As operations in the Permian expanded, the DSC stood up a performance pod in 2019 to focus on drilling cycle time and costs. Several analytical tools were developed in collaboration with business partners to meet the unique needs of shale operations. To streamline operations and provide the best support possible to business units, directional service providers and G&G ops teams decided to physically co-locate within the DSC. Placing directional drillers, measurement while drilling (MWD) personnel, and geosteerers in the DSC improved collaboration, reduced costs, and provided an additional safety benefit by removing personnel from rig sites. Today, the DSC is organized by asset class—unconventional resources, deepwater, etc. — so that teams can easily share lessons learned and leverage performance improvement opportunities across regions. Successful DSC pods require streamlined workflows and software tools. The high volume of data from downhole and surface sensors substantiated the need for mature digital tools. Furthermore, the shortage of experienced well professionals in the industry presented challenges for identifying consistent operational outcomes. The Wells DSC continues to develop in-house workflows and analytical tools while working with service companies to unleash the power of data to improve performance, enhance decision quality, reduce costs, and improve cycle time.
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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".