MétaCan
Menu
Back to cohort
Record W2009021062 · doi:10.2118/0613-0126-jpt

Real-Time Optimization of SAGD Operations

2013· article· en· W2009021062 on OpenAlexaboutno aff
Dennis Denney

Bibliographic record

VenueJournal of Petroleum Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsInjectorSteam-assisted gravity drainagePetroleum engineeringSteam injectionOil sandsProcess (computing)Process engineeringAsphaltEnhanced oil recoveryEnvironmental scienceEngineeringComputer scienceMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 157923, ’Real-Time Optimization of SAGD Wells,’ by Luis E. Gonzalez, SPE, Peter Ficocelli, SPE, and Tad Bostick, SPE, Weatherford, prepared for the 2012 SPE Heavy Oil Conference Canada, Calgary, 12-14 June. The paper has not been peer reviewed. The steam-assisted-gravity-drainage (SAGD) process along with an efficient steam-use process can reduce production costs and increase the oil-recovery rate. The use of real-time downhole monitoring is an effective approach to achieve this optimization. The use of downhole distributed-temperature sensing (DTS) and array-temperature sensing by use of fiber-optic technology has led to instrumented wells that enable data access on a real-time basis, leading to better control of operations, and to optimizing the steam process. Also, fiber-optic technology enables measuring pressure and temperature over the same fiber and in close proximity along the wellbore. Introduction At reservoir conditions, bitumen is essentially immobile, and recovery of these highly viscous fluids requires viscosity reduction, often by applying heat. The SAGD process is an effective recovery method for heavy oil and bitumen. The SAGD process typically uses two parallel horizontal wells that normally are separated vertically by approximately 5 m. The top well is the steam injector, and the bottom well is the producer. As steam is injected, a steam chamber will grow around and above the injection well. Despite its effectiveness, the SAGD process has many economic risks including a high initial investment for constructing the surface facilities and uncertainties related to product prices. Displacement efficiency is a critical factor in controlling oil recovery from the steam chamber, and, although increased volumetric sweep is beneficial, the chamber-growth rate greatly affects the rate at which the displacement efficiency increases in the steam chamber. Increasing the volume of the steam chamber too quickly may decrease the effectiveness of the SAGD process. Uniform distribution of steam along the entire length of the wellbore could lead to a more-uniform steam-chamber growth, making the entire length of the well productive and developing optimum performance of a SAGD well pair. Traditionally, the SAGD injection well is completed with dual injection strings (one injection string ending at the heel and the other injection string ending at the toe) and the production well is completed with a slotted liner, as shown in Fig. 1. Steam distribution along the well is governed by different factors. The most important factors include pressure in the injector and producer wells, fluid-flow regime, fluid velocity, wellbore trajectory, and heat loss along the length of the wellbore. A new well/injector configuration is proposed that includes steam diverters, inflow-control devices, centralizers, fiber-optic pressure gauges, a fiber-optic DTS system, and control lines. An inter-mediate configuration could be the traditional dual string with the addition of temperature and pressure measurement by use of fiber-optic technology to help control the shape of the steam chamber.

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.

How this classification was reachedexpand

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: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.235
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Petroleum TechnologySame topicReservoir Engineering and Simulation MethodsFrench-language works237,207