Discussion on Integrating Monitoring Data into the Reservoir Management Process

Rossi, David J.; Gurpinar, Omer; Nelson, R.; Jacobsen, Stine Lindahl

doi:10.2118/65150-ms

Cited by 19 publications

(4 citation statements)

References 12 publications

(3 reference statements)

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“…This real-time data is now routinely used for reservoir monitoring, flow assurance calculations, production optimization 8,9,10 and for reservoir engineering analysis 11 . However, despite many good intentions 12,13 , reservoir modeling has been slow to take up the use of real-time production data to condition its models. While there have been some promising projects using Ensemble Kalman Filtering to update reservoir models 14,15 , these have mostly been research oriented and have not yet made it into mainstream use.…”

Section: Introductionmentioning

confidence: 99%

Rapid Model Updating with Right-Time Data—Ensuring Models Remain Evergreen for Improved Reservoir Management

Webb¹,

Revus²,

Myhre³

et al. 2008

All Days

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History matching reservoir models to production data has been a challenge for asset teams since the early days of reservoir simulation. Keeping these models evergreen as production data continues to arrive, knowing when a rehistory match is required and being able to re-history match easily and efficiently is also a major challenge which is often not addressed in a timely manner. This need is becoming even more pressing as real-time reservoir performance data is increasingly available. Decisions can now be made with the support of the good quality real-time data from the reservoir usually in the form of pressure data from downhole gauges and rate data from multiphase meters. With the recent integration of existing technologies, a rapid model updating workflow is now possible. The history matching workflow that was once a discrete process can now be a computer assisted continuous process. Using statistically-based assisted historymatching technology in conjunction with real-time data acquisition, data monitoring, and reservoir simulation software, new production data can be quickly assimilated into the reservoir model. Real-time data from the field measurement devices is filtered for consumption by the reservoir modeling software and compared with the forecasts from the reservoir simulator to determine if rehistory matching is required. The new data can be added to the history file and the model (revised if necessary) can be used in operational performance optimization. This rapid model updating workflow can be run semiautomatically on a continuous basis as new production data is gathered, thereby keeping reservoir models evergreen and providing the most up-to-date basis for the making of important reservoir management decisions.

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Section: Introductionmentioning

confidence: 99%

Rapid Model Updating with Right-Time Data—Ensuring Models Remain Evergreen for Improved Reservoir Management

Webb¹,

Revus²,

Myhre³

et al. 2008

All Days

View full text Add to dashboard Cite

show abstract

“…Along with the advent of reliable permanent down-hole gauge available to the oil industry, the importance of continuous reservoir monitoring in mature field and its value delivered by up-to-date information has been recognized as the essential element in modern reservoir management (Athichanagom et al, 1999;Rossi et al, 2000;Chiriti et al, 2001;Ballinas and Owen, 2002;Haddad et al, 2004;Olsen and Nordtvedt, 2005;Weaver et al, 2005;Frota and Destro, 2006;Olsen and Nordtvedt, 2006). However, the key to ensure the information delivered correctly is a robust analysis method, which can, not only handling the large, noisy data set, but also distil the "message" from the dynamic data for right decision making.…”

Section: Introductionmentioning

confidence: 99%

Transient Pressure Analysis of 4D Reservoir System Response From Permanent Down Hole Gauges (PDG) for Reservoir Monitoring, Testing, and Management

Zheng¹,

Li²

2007

Proceedings of Asia Pacific Oil and Gas Conference and Exhibition

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fax 01-972-952-9435. AbstractOne of the key objectives doing well testing is to derive effective reservoir properties, such as permeability, to provide input for reservoir simulation. Traditional approach in well test analysis is first, to separate transient pressure draw down and build ups' due to constant flowing rate, then analyze them by forcing a pre-selected model to derive effective reservoir permeability through an inversion process. However, the results obtained this way are not really dynamic or at most is "pseudo-dynamic", because these are based on the fact that a continuous signal was broken into discontinuous signals. Some information (more than a changed permeability value) of the reservoir system response may already lose, while uncertainties were increased or multiplied in the process of data evaluation and analysis. This paper presents a new approach, which will allow for continuous reservoir system response analysis and interpretation. Therefore the information derived from such analysis can reflect the real time, dynamic changes of the reservoir. This has been proved more powerful in analyzing transient pressure, particularly for that from permanent down hole gauges (PDG).Numerical well testing procedures, assisted by neural network method, were used to analyze the data from simulated and field cases in a continuous, systematic fashion. First, the flowing rate history was recovered from the measured transient pressure. Transient pressure histories are then reproduced through simulations of both well test forward model and neural network black box model. A match between measured pressure response (PDG data) and re-produced transient pressure histories will then be made, to achieve the ultimate goal of well testing -real time reservoir monitoring, model calibration and reservoir management. This final matching process was named "guided (by neural network model) history matching".

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“…This scenario change has suggested an adaptation on reservoir management workflow process 4 attempting to assimilate this data as information into reservoir description (Fig. Rossi et al 5 proposed a reservoir management workflow that takes into account this high frequency data acquisition. Rossi et al 5 proposed a reservoir management workflow that takes into account this high frequency data acquisition.…”

Section: Introductionmentioning

confidence: 99%

“…Rossi et al 5 proposed a reservoir management workflow that takes into account this high frequency data acquisition. The schematic representation of the process workflow 5 does not capture integrally the iterative process involved in the reservoir management. The new workflow operates at multiple and parallel time-space scales.…”

Section: Introductionmentioning

confidence: 99%

Reservoir Management Optimization Using Permanent Downhole Gauge Data

Silva

Kato

2004

SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractPetroleum field exploitation on deep-water environment requires well completion strategy that minimizes the number of workover operations and provides an efficient reservoir data acquisition system to support field management decisions. This paper presents a Petrobras field experience in integrating monitoring data into reservoir management optimization on Campos Basin, Brazil. The field development plan comprised satellite wells completed with wet-Xmas tree and producing with independent flowlines to a FPSO. To ensure an adequate monitoring data program 1 , all wells were equipped with permanent temperature and pressure sensors at downhole (PDG), wet-Xmas tree and production header. The anticipated production program generated a huge highfrequency database during the initial field development plan implementation. Additional time-lapse data obtained from new drilled wells during field development increased reservoir knowledge about reservoir depletion, compartmentalization, barriers, waterflood efficiency, etc. Until nowadays data from PDG and time-lapse data allow frequent updates of the geological and reservoir flow model, reducing the project uncertainties. The challenge is to incorporate these data into reservoir management optimization at the right time without loosing the economic opportunity window. The strategy adopted has been considered efficient to face the preeminent decision making required by this type of reservoir in a deep-water field under anticipated production.

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Discussion on Integrating Monitoring Data into the Reservoir Management Process

Cited by 19 publications

References 12 publications

Rapid Model Updating with Right-Time Data—Ensuring Models Remain Evergreen for Improved Reservoir Management

Rapid Model Updating with Right-Time Data—Ensuring Models Remain Evergreen for Improved Reservoir Management

Transient Pressure Analysis of 4D Reservoir System Response From Permanent Down Hole Gauges (PDG) for Reservoir Monitoring, Testing, and Management

Reservoir Management Optimization Using Permanent Downhole Gauge Data

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