MétaCan
Menu
Back to cohort
Record W2063778491 · doi:10.2523/iptc-13346-ms

Staged design of an EOR pilot

2009· article· en· W2063778491 on OpenAlex
Bhargaw Adibhatla, Robert Chick Wattenbarger

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Petroleum Technology Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersExxon Mobil Corporation
KeywordsProcess (computing)Computer scienceScheduleEnhanced oil recoveryLead (geology)Risk analysis (engineering)Systems engineeringPilot plantEngineeringPetroleum engineering

Abstract

fetched live from OpenAlex

Abstract Due to the complexity and uncertainty associated with most enhanced oil recovery (EOR) processes, a small-scale pilot is often needed to demonstrate the successful application of an EOR process within a specific reservoir prior to wider commercial implementation. To help manage the complexity and competing cost, schedule, and technical priorities of a pilot, a systematic approach to planning and designing a pilot has been developed. The approach, which is described in this paper, covers various subsurface activities necessary for design of an EOR pilot. Issues related to facilities and other aspects critical to pilot success are also addressed, but in less detail. The sequence of these activities is described and managed in defined stages. The relationship between various activities within a given stage is described using an activity matrix. The activity matrix has proven to be a useful tool for planning and prioritizing various pilot activities. Examples of specific items in the staged approach to EOR pilot design are provided. Introduction A well-designed pilot can be a key element in the successful commercial application of an EOR process. A poorly designed pilot can be costly and lead to an incorrect commercial decision, long delays, or a failed implementation. To help manage the complexity and challenges associated with EOR pilots, guidelines were developed that describe various recommended activities for pilot design. The guidelines, which are summarized in this paper, are designed to be fairly broad and describe recommended activities that are applicable to most EOR pilots (e.g., thermal, gas, chemical). As such, the guidelines serve as a starting point for project-specific guidelines that should be customized for the specific process, field, and pilot business needs. The staged process for pilot design reflects experiences from ExxonMobil's own studies and applications of EOR pilots as well as the published experiences of others. ExxonMobil has piloted several EOR processes throughout the last 40 years. Some examples include chemical processes at the Loudon field1–7 in the United States, the Pembina field8 in Canada, the West Yellow Creek field9 in the United States, steamflood and LASER applications10–12, steam-foam applications13–14, CO2 flooding at the Means field in the United States15, Solid Stabilized Emulsions (SSE)16 in Canada, piloting of miscible gas injection at Judy Creek17–18 in Canada, and others19–21. The material presented in this paper builds upon the lessons learned from these pilots. This paper is not intended to describe the overall process for evaluating and implementing an EOR process, nor is it meant to summarize best practices for EOR pilots. These are described elsewhere22–23. Rather, the approach to pilot design described in this paper is meant to serve as an overall guide to planning and properly sequencing the activities associated with EOR pilot design. These activities include the cross-functional interaction between reservoir engineers, facilities engineers, surveillance engineers, geoscientists, and other disciplines expected in any field development activity. As much as possible, the guidelines focus on the activities that are specific to EOR pilots, leveraging as much as possible existing project management procedures and best practices. EOR Staged Evaluation and Development Process Pilot design and implementation is part of a broader workflow for evaluating and implementing an EOR process for a particular field. The overall process, which has been described in earlier publications22–23, is briefly summarized in the next section. An overview of a staged process to evaluate and implement EOR processes for a specific field is shown in Figure 1.22 EOR evaluation starts with initial data collection, identification of potential EOR recovery processes, and screening economics. After initial screening, promising EOR processes are evaluated in greater detail through laboratory experiments and detailed modeling studies. If the results of the in-depth analysis indicate economic benefit, a field pilot may be performed to address key uncertainties. The decision to pilot, requirements of a successful pilot, types of field pilots, and other piloting best practices have been described elsewhere22. If the pilot is successful and the EOR process remains economically attractive, then the pilot is followed by commercial application of the EOR process on a wider scale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.453
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.285
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it