Stochastic Modeling Technique as Applied to History Matching of Eocene-Misoa Reservoir, Lake Maracaibo, Venezuela

Jimenez, Z.; Azpiritxaga, I.; Lozada, T.

doi:10.2523/38020-ms

Cited by 3 publications

(1 citation statement)

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“…Many other methods have been studied and applied to the history-matching problem. Examples include optimal control theory, [19][20][21][22] stochastic modeling techniques, [23][24][25][26] sensitivity analysis techniques, [27][28][29] and gradual deformation method. 31 In Refs.…”

Section: Introductionmentioning

confidence: 99%

Errors in History Matching

2004

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The usual procedure in history matching is to adopt a Bayesian approach with an objective function that is assumed to have a single simple minimum at the "correct" model. In this paper, we use a simple cross-sectional model of a reservoir to show that this may not be the case. The model has three unknown parameters: high and low permeabilities and the throw of a fault. We generate a large number of realizations of the reservoir and choose one of them as a base case. Using the weighted sum of squares for the objective function, we find both the best production-and best parameter-matched models. The results show that a good fit for the production data does not necessarily have a good estimation for the parameters of the reservoir, and therefore it may lead to a bad forecast for the performance of the reservoir. We discuss the idea that the "true" model (represented here by the base case) is not necessarily the most likely to be obtained using conventional history-matching methods. Reservoir Model and Objective FunctionWe use a simple 2D cross-sectional model of a layered reservoir, as shown in Fig. 1. 57 The model is 2D, with no-flow boundary conditions. Water is injected on the left side, and a production well is located on the right. There is a single, normal fault at the midpoint between the two wells. The reservoir contains six alternating layers of good-and poor-quality sands with high and low permeabilities, respectively. The porosities of the good-and poor-quality sands are 0.30 and 0.15, respectively. We also assume that the three good-quality layers have identical properties, and the three poor-quality layers have a different set of identical properties. The thickness of the layers has an arithmetic progression, with the top layer having a thickness of 12.5 ft, the bottom layer a thickness of 7.5 ft, and with a total thickness of 60 ft. The width of the model

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Section: Introductionmentioning

confidence: 99%

Errors in History Matching

2004

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A review of automatic history matching

Abdulrazzaq

Hasan

2023

Materials Today: Proceedings

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An Innovative Model Preconditioning Approach for Fast and Reliable History Matching

Kayode,

Stephen,

Albeshri

2024

Day 2 Tue, February 13, 2024

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History matching is the process of calibrating a geological model to match the historical production, pressure and other reservoir characterization data. The methods developed in this work aims to address the history matching problem through an a priori integration of data-derived features into the static modeling phase, using a process we have termed pre-conditioned modelling. The methodology combines data mining techniques with intelligent visualizations to swiftly extract features from historical pressure and core permeability data, and integrate these features a priori into the static model. An anomaly detection algorithm was developed and used to detect data outliers and exclude anomalies from pressure data. A pattern recognition algorithm was developed, used for pressure cluster differentiation, and then to create reservoir regions containing wells within similar time-lapse pressure cluster, and the created regions were used as containers to guide permeability modeling and for applying sub-global permeability updates during history matching. The reservoir's internal architecture was derived from core permeability using a data mining algorithm we developed and termed fixed-window averaging. The intra-reservoir architecture was used to control the static model's vertical heterogeneity trends. This innovative approach to rapidly integrate historical pressure and core permeability dataset features into the static modeling process, resulted in a model that required no significant need for the traditional history-match process because the do-nothing match was excellent. A data analytics algorithm termed spatial Pythagorean search was developed and used to detect the causes of well productivity problems. An advanced visualization interface was developed which allows both time-based (e.g., water-cut, datum pressure) and depth-based (e.g., FTS, PNL, mobility, PTA) parameters to be displayed, permitting fully integrated decision making regarding necessary modifications. Leveraging holistic data visualization and data mining techniques, our methodology helps establish a reliable reservoir model by prior integration of data-derived features into the static model, significantly reducing the time required for history matching.

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Stochastic Modeling Technique as Applied to History Matching of Eocene-Misoa Reservoir, Lake Maracaibo, Venezuela

Cited by 3 publications

References 0 publications

Errors in History Matching

Errors in History Matching

A review of automatic history matching

An Innovative Model Preconditioning Approach for Fast and Reliable History Matching

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