Efficient Workflow for Assisted History Matching and Brownfield Design of Experiments for the Tengiz Field

Kassenov, Baurzhan; King, Gary; Chaudhri, Moon; Abdrakhmanova, Aizada; Jenkins, Steve; Bateman, Phil; Iskakov, Elrad

doi:10.2118/172329-ms

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…By dividing the range of the attribute into intervals, the number of combinations among attributes increases significantly. One solution to this problem is to use sensitivity analyses to select the most influential attributes and reduce the search space (Almeida Netto et al, 2003;Rotondi et al, 2006;Kassenov et al, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Costa

Maschio

Schiozer

2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Accurately characterizing fractures is complex. Several studies have proposed reducing uncertainty by incorporating fracture characterization into simulations, using a probabilistic approach, to maintain the geological consistency, of a range of models instead of a single matched model. We propose a new methodology, based on one of the steps of a general history-matching workflow, to reduce uncertainty of reservoir attributes in naturally fractured reservoirs. This methodology maintains geological consistency and can treat many reservoir attributes. To guarantee geological consistency, the geostatistical attributes (e.g., fracture aperture, length, and orientation) are used as parameters in the history matching. This allows us to control Discrete Fracture Network attributes, and systematically modify fractures. The iterative sensitivity analysis allows the inclusion of many (30 or more) uncertain attributes that might occur in a practical case. At each uncertainty reduction step, we use a sensitivity analysis to identify the most influential attributes to treat in each step. Working from the general history-matching workflow of Avansi et al. (2016), we adapted steps for use with our methodology, integrating the history matching with geostatistical modeling of fractures and other properties in a big loop approach. We applied our methodology to a synthetic case study of a naturally fractured reservoir, based on a real semi-synthetic carbonate field, offshore Brazil, to demonstrate the applicability in practical and complex cases. From the initial 18 uncertain attributes, we worked with only 5 and reduced the overall variability of the Objective Functions. Although the focus is on naturally fractured reservoirs, the proposed methodology can be applied to any type of reservoir.

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Section: Literature Reviewmentioning

confidence: 99%

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Costa

Maschio

Schiozer

2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…Similarly, Becerra et al (2011) used sensitivity analysis in an approach to reduce the uncertainty in a history matching problem. Kassenov et al (2014) showed an application of sensitivity analysis with design of experiments in the Tengiz field.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs

Costa

Maschio

Schiozer

2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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History matching for naturally fractured reservoirs is challenging because of the complexity of flow behavior in the fracture-matrix combination. Calibrating these models in a history-matching procedure normally requires integration with geostatistical techniques (Big Loop, where the history matching is integrated to reservoir modeling) for proper model characterization. In problems involving complex reservoir models, it is common to apply techniques such as sensitivity analysis to evaluate and identify most influential attributes to focus the efforts on what most impact the response. Conventional Sensitivity Analysis (CSA), in which a subset of attributes is fixed at a unique value, may over-reduce the search space so that it might not be properly explored. An alternative is an Iterative Sensitivity Analysis (ISA), in which CSA is applied multiple times throughout the iterations. ISA follows three main steps: (a) CSA identifies Group i of influential attributes (i = 1, 2, 3, …, n); (b) reduce uncertainty of Group i, with other attributes with fixed values; and (c) return to step (a) and repeat the process. Conducting CSA multiple times allows the identification of influential attributes hidden by the high uncertainty of the most influential attributes. In this work, we assess three methods: Method 1 – ISA, Method 2 – CSA, and Method 3 – without sensitivity analysis, i.e., varying all uncertain attributes (larger searching space). Results showed that the number of simulation runs for Method 1 dropped 24% compared to Method 3 and 12% to Method 2 to reach a similar matching quality of acceptable models. In other words, Method 1 reached a similar quality of results with fewer simulations. Therefore, ISA can perform as good as CSA demanding fewer simulations. All three methods identified the same five most influential attributes of the initial 18. Even with many uncertain attributes, only a small percentage is responsible for most of the variability of responses. Also, their identification is essential for efficient history matching. For the case presented in this work, few fracture attributes were responsible for most of the variability of the responses.

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An Iterative Work Flow for History Matching by Use of Design of Experiment, Response-Surface Methodology, and Markov Chain Monte Carlo Algorithm Applied to Tight Oil Reservoirs

Wantawin

Sepehrnoori

2017

SPE Reservoir Evaluation &Amp; Engineering

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Summary Although a large amount of wells has been extensively drilled to develop unconventional reservoirs, uncertainty associated with reservoir and fracture properties can significantly affect the evaluation of an individual well's performance with simulations. From the history-matching point of view, this uncertainty causes nonuniqueness of solutions that should be obtained with a robust probabilistic method without overly exhausting simulation resources. Because of the challenge in this work, a proxy-based history-matching approach can deliver unique advantages of reducing the computational requirement and providing probabilistic interpretation. The work flow presented in this paper is designed to exploit full ranges of proxy-modeling benefits from prioritizing significant reservoir properties, estimating the variation of model responses, and searching multiple history-matching solutions for reliable probabilistic forecasts. A screening process is introduced at the initial stage of the work flow with design of experiment (DOE) and response-surface methodology (RSM), so the dimensions of proxy models can be reduced. In addition, the proxy models progress through the work flow by iterations that aim to improve their accuracy. This iterative work flow efficiently uses simulation resources by use of all the completed runs to provide more information about the model responses for the subsequent iterations. While the work flow is being iterated, multiple proxy models are explored for history-matching solutions by a Markov chain Monte Carlo (MCMC) algorithm. Then, history-matching solutions are used to evaluate the probability distribution of the long-term estimated ultimate recovery (EUR). Finally, an application of the work flow to a horizontal well in the Middle Bakken is presented in this paper.

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Efficient Workflow for Assisted History Matching and Brownfield Design of Experiments for the Tengiz Field

Cited by 6 publications

References 7 publications

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs

An Iterative Work Flow for History Matching by Use of Design of Experiment, Response-Surface Methodology, and Markov Chain Monte Carlo Algorithm Applied to Tight Oil Reservoirs

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