Reinventing Deepwater Exploratory Testing
Bibliographic record
Abstract
Abstract Operators face numerous challenges when conducting deepwater operations, including hostile downhole environments, ever-increasing well and water depths, complex logistics and planning and excessive costs. As it pertains to exploratory well testing, most systems and equipment that are capable and reliably operate within this environment are valuable, but costly. In this arena, time is a commodity that is quantified in dollars, not minutes. Many exploration wells that deserve thorough analysis and measure—typically provided by a well-designed testing program and subsequent nodal and reservoir analysis—have settled for an incomplete data picture provided by logs, seismic data, and formation testers. For many Gulf of Mexico operators, the economic and logistical challenges have outweighed the benefit of data gleaned through well testing. In addition, the widening gap between these difficult environments and the capabilities of the equipment designed to control them is challenging our industry's ability to harness the yields within these frontiers. We propose a new way of testing exploratory wells using the world's first openhole deepwater exploratory testing system that will isolate each target interval, selectively flow and shut-in zones with radio-frequency identification (RFID) technology, acquire shut-in pressure-buildup data by acoustic telemetry to enable subsequent zones to flow while acquiring shut-in formation buildup data on the previous zones—all with robust completion equipment. Vulnerable, O-ring-laden, pressure-operated, limited-capability drillstem testing equipment is not meeting the current challenges of deepwater exploration. Exploratory well tests that were not previously viable are now well within reach. The system is run to depth as a completion-testing liner system, requiring no string manipulation once set and is replete with contingency capability. The proposed system and method will deliver the data needed to answer the reservoir questions asked by our operators within the industry that are exploring at the limits of technology. This document outlines the challenges, system components and operating sequence, and considers the cost, time, data and risk-reduction benefits of the new system versus yesterday's methods. Introduction Exploratory well testing is desirable in new discovery fields because it provides data crucial to characterizing the reservoir. Peripheral advantages of well testing in an exploratory situation include validation and calibration of other data against well testing data, accurate population of simulators, improved certainty of the producing zones' content and quantity, and allows a more focused direction regarding future appraisal and development activities. Many operators choose to forgo well testing opportunities because of resources and time required, and inherent risks involved in flow tests. The system is designed to address the challenges that make the execution of a well test prohibitive, and allow the operator to realize the value of a comprehensive depiction of the new reservoir. Specifically, the system was designed to improve zonal isolation and flow barriers, minimize trips in hole and pipe manipulation, and eliminate well intervention. Enhancement of well control was considered paramount.
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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".