How Routine Reservoir Surveillance with Neural Networks and Simplified Reservoir Models can Convert Data into Information

Jonge, Gert J. de; Stundner, Michael

doi:10.2118/78334-ms

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…AI&DM contributes to all aspects of the E&P industry to help increase effi ciency and productivity (Tapias et al 2001;Saputelli et al 2002;Zangl and Oberwinkler 2004;Finol and Buitrago 2002;deJonge and Stundner 2002). AI&DM provides a key component of the emerging smart fi eld.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence and Data Mining: Enabling Technology for Smart Fields

Mohaghegh

2009

The Way Ahead

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FIG. 1 Schematic diagram of the closed-loop RTRM.mart completions let engineers intervene with details of wells' operations from a distance. Smart wells transmit nearly continuous (real-time) data streams (pressure, fl ow rate, etc.) to the remote offi ce, providing immediate feedback on the consequences of recent decisions made and actions taken. Smart fi elds include multiple smart wells providing the possibility of managing the entire reservoir remotely and in real time. Our industry is now on the eve of making real-time reservoir management (RTRM) a reality. Artifi cial intelligence and data mining (AI&DM) is one of the key enabling technologies for RTRM. AI&DM enables us to process, model, and use real-time data streams, build accurate prototypes of sophisticated reservoir-simulation models that can respond to changes in model input in real time to help us make crucial reservoir management decisions and close the loop on highfrequency feedback to the reservoir model for making RTRM a reality.In short, the contribution of AI&DM technology to smart fi elds can be summarized in two items: (a) It provides the technological capabilities to automatically and autonomously handle high-frequency data streams received from permanent downhole gauges (data cleansing, data summarization, pattern recognition, adaptive online modeling, etc.); (b) It provides the modeling framework and workfl ows that allows the existing reservoirsimulation models to be run in real time, thus making RTRM possible. RT R MReservoir management is defi ned as the practical science of developing a hydrocarbon fi eld in a manner that maximizes ultimate recovery. RTRM involves performing reservoir engineering analysis in real time to support frequent fi eld-management decisions, and using near-real-time feedback on the consequences from the reservoir/well to assess management decisions and reservoir performance. Therefore, RTRM is the enabling technology for the emerging smart fi elds. It is the closed-loop process during which the reservoir model is continuously updated by the information/ feedback received from the fi eld (by means of high-frequency data streams) that are the consequence of the decisions made and implemented, based on the reservoir model.The ultimate benefi t of the smart fi eld depends on the degree of our success in building and implementing RTRM. In other words, the value of highfrequency data streams is realized once we are able to use them in effectively updating the reservoir model and subsequently using the model to make fi eld-operational decisions. Therefore, the key to moving toward successful smart-fi eld operation is to be able to perform the following steps: Acquire, process, and analyze real-time, continuous data streams from the wells.These real-time pressure and rate data provide indications of reservoir reaction to the operational decisions made by engineers using the reservoir model. Use the real-time data as feedback to the reservoir model.By analyzing the high-frequency data in the context of the reservoir mo...

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence and Data Mining: Enabling Technology for Smart Fields

Mohaghegh

2009

The Way Ahead

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“…Most optimization efforts in the oil field today target a single process (gas lift optimization, water injection optimization). Recent advances in this area use novel methodology such as genetic algorithms 16 and neural networks [17][18] . Future optimization capabilities, in the form of integrated work-processes and decision support tools, will allow optimization across several processes.…”

Section: T Comparison Of Cera and Chevron Resultsmentioning

confidence: 99%

“…Use of neural networks and simplified data models was combined with routine reservoir surveillance to cleanse and aggregate data, deliver comparisons of performance against prediction and provide alarms and triggers. This has resulted in significant decrease of time spent on aggregating and analyzing data, and has significantly accelerated decisionmaking processes within reservoir management 17 This concept of collaboration in all phases of technology development is illustrated in Figure 5. The road from a corp actual deployment projects in oil and gas assets can provide considerable challenges.…”

Section: El Trapial Field Argentina and Fradementioning

confidence: 99%

Real-Time Asset Management: From Vision to Engagement—An Operator's Experience

Unneland

Hauser

2005

SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe Digital Oil Field is rapidly gaining attention within the oil and gas industry. Several oil and gas operators are working to develop their vision for an oil field of the future, testing new technologies, setting up programs and participating in industry events. The vision is an integrated approach allowing more real-time control of asset management. Many different names are used in the upstream industry to describe this trend; Smart Fields, Digital Oil Field, Next Generation Oilfield, Field of the Future, e-field, Instrumented Field and Intelligent Energy. However, there is still uncertainty as to what needs to be done and what value it will actually bring to the industry. Several operators and service companies are transitioning from the initial envisioning and abstract phase to projects creating measurable value for the company. In this paper, Chevron's efforts within the Digital Oil Field domain, including concept, business case, corporate governance, technology development, partnerships, deployment and field experience are presented.

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“…This workflow [11] can be used to either investigate uncertainties of designated input parameters, to optimize operational settings, or shorten the time to history match the numerical model.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Gas Storage Management

2006

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TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractAn approach is investigated, to reduce the amount of CPU time needed to execute a numerical full field model in an optimization loop.To demonstrate the power of this approach, a real life example is presented. Data from a gas storage reservoir have been used to setup a single tank material balance program. Then, a limited number of simulation runs is carried out. These simulation runs are intended to span over the whole range of input parameter variation (app. 25 runs).In a next step, a Neural Network (NN) model is setup. By training a Neural Network on the so gained simulation outputs, a model, which is able to interpolate between the individual simulation scenarios is created. In this way, a large variety of different scenarios can be represented with a limited amount of model runs.The trained Neural Network model is used as a proxy function for an optimization routine. The trained Neural Network has been used as fitness function for the Genetic Algorithm to minimize the output parameter, which is in this example the RMS-error of measured and calculated tank pressure. Due to the very low CPU consumption of the Neural Network, a large number of realisations can be calculated in a short amount of time. By this, the absolute minimum of the desired output parameter (in this case the RMS-error) can be evaluated in a few seconds.The Genetic Algorithm has succeeded to find a minimum, which is located very close to the absolute minimum of all possible solutions.

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How Routine Reservoir Surveillance with Neural Networks and Simplified Reservoir Models can Convert Data into Information

Cited by 14 publications

References 3 publications

Artificial Intelligence and Data Mining: Enabling Technology for Smart Fields

Artificial Intelligence and Data Mining: Enabling Technology for Smart Fields

Real-Time Asset Management: From Vision to Engagement—An Operator's Experience

Application of Artificial Intelligence in Gas Storage Management

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