Promoting Real-Time Optimization of Hydrocarbon Producing Systems
Bibliographic record
Abstract
Promoting Real-Time Optimization of Hydrocarbon Producing Systems L.A. Saputelli; L.A. Saputelli PDVSA Search for other works by this author on: This Site Google Scholar S. Mochizuki; S. Mochizuki ExxonMobil Search for other works by this author on: This Site Google Scholar L. Hutchins; L. Hutchins BP Search for other works by this author on: This Site Google Scholar R. Cramer; R. Cramer Shell Search for other works by this author on: This Site Google Scholar M.B. Anderson; M.B. Anderson Schlumberger Search for other works by this author on: This Site Google Scholar J.B. Mueller; J.B. Mueller Schlumberger Search for other works by this author on: This Site Google Scholar A. Escorcia; A. Escorcia Halliburton Search for other works by this author on: This Site Google Scholar A.L. Harms; A.L. Harms ConocoPhillips Search for other works by this author on: This Site Google Scholar C.D. Sisk; C.D. Sisk BP Search for other works by this author on: This Site Google Scholar S. Pennebaker; S. Pennebaker ITSVE Search for other works by this author on: This Site Google Scholar J.T. Han; J.T. Han ITSVE Search for other works by this author on: This Site Google Scholar A. Brown; A. Brown EPS Search for other works by this author on: This Site Google Scholar C.S. Kabir; C.S. Kabir ChevronTexaco Search for other works by this author on: This Site Google Scholar R.D. Reese; R.D. Reese Case Services Search for other works by this author on: This Site Google Scholar G.J. Nunez; G.J. Nunez ITSVE Search for other works by this author on: This Site Google Scholar K.M. Landgren; K.M. Landgren Schlumberger Search for other works by this author on: This Site Google Scholar C.J. McKie; C.J. McKie EPS Search for other works by this author on: This Site Google Scholar C. Airlie C. Airlie EPS Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Offshore Europe Oil and Gas Exhibition and Conference, Aberdeen, United Kingdom, September 2003. Paper Number: SPE-83978-MS https://doi.org/10.2118/83978-MS Published: September 02 2003 Connected Content Related to: Promoting Real-Time Optimization of Hydrocarbon-Producing Systems Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Saputelli, L.A., Mochizuki, S., Hutchins, L., Cramer, R., Anderson, M.B., Mueller, J.B., Escorcia, A., Harms, A.L., Sisk, C.D., Pennebaker, S., Han, J.T., Brown, A., Kabir, C.S., Reese, R.D., Nunez, G.J., Landgren, K.M., McKie, C.J., and C. Airlie. "Promoting Real-Time Optimization of Hydrocarbon Producing Systems." Paper presented at the SPE Offshore Europe Oil and Gas Exhibition and Conference, Aberdeen, United Kingdom, September 2003. doi: https://doi.org/10.2118/83978-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Offshore Europe Conference and Exhibition Search Advanced Search AbstractThe term "Real-Time Optimization" (RTO) has rapidly found its way into common usage in the oil and gas industry, as it already has in many others. However, RTO in the oil and gas industry is usually used more as a slogan rather than describing a system or process that truly optimizes anything at all, let alone does so in real-time.In this paper, we describe what RTO means in the exploitation of hydrocarbons and what technologies are available now and are likely to be available in the future. We discuss how it is misunderstood and what real financial benefits await those who adopt it. Furthermore, we are working toward developing a method of classification to allow us to establish where a field operation lies on the RTO ladder, and to help plan a strategy to generate the benefits that moving up the RTO ladder can offer on specific fields and assets. The paper also describes a new SPE Technical Interest Group (TIG), explaining why it has been formed, and outlining its objectives and some planned deliverables.Real-time Optimization - Concepts and DefinitionsWhat is optimization?Intuitively most people agree on what we mean by "optimize." This comes down to understanding the dictionary definition; that is, to make the most of; to plan or carry out an economic activity with maximum efficiency; to find the best compromise among several often conflicting requirements, as in engineering design. Therefore, examples of what is usually meant by optimization in the oil and gas industry include:Maximizing hydrocarbon production or recovery,Finding the best solution in the region of physical and financial constraints to produce a decision,Maximizing net present value (NPV) through changes in capital expenditure (CAPEX) and/or operational expenses (OPEX). These elements, in turn, improve financial efficiency in portfolio management and risk analysis, andAdvanced real-time optimization: behavioral prediction and inference, pattern recognition to identify states of a group of wells, continuous adaptation and self-tuning ability.Although we may readily agree on these (and other) descriptions of what would be the outcome of optimization, agreeing what it actually means appears to be more complex. The reason for this is that the term optimization is usually used very loosely, whereas it needs to be defined rigorously and mathematically, while honoring the real-life physical system constraints that exist in the overall production process. Keywords: constraint, artificial intelligence, modeling, society of petroleum engineers, classification, sensor, data acquisition, application, real-time optimization, production optimization Subjects: Information Management and Systems, Artificial intelligence This content is only available via PDF. 2003. 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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".