Fractured reservoir history matching improved based on artificial intelligent

Summary Fractures are common features in many well-known reservoirs. Naturally fractured reservoirs include fractured igneous, metamorphic, and sedimentary rocks (matrix). Faults in many naturally fractured carbonate reservoirs often have high-permeability zones, and are connected to numerous fractures that have varying conductivities. Furthermore, in many naturally fractured reservoirs, faults and fractures can be discrete (rather than connected-network dual-porosity systems). In this paper, we investigate the pressure-transient behavior of continuously and discretely naturally fractured reservoirs with semianalytical solutions. These fractured reservoirs can contain periodically or arbitrarily distributed finite- and/or infinite-conductivity fractures with different lengths and orientations. Unlike the single-derivative shape of the Warren and Root (1963) model, fractured reservoirs exhibit diverse pressure behaviors as well as more than 10 flow regimes. There are seven important factors that dominate the pressure-transient test as well as flow-regime behaviors of fractured reservoirs: (1) fractures intersect the wellbore parallel to its axis, with a dipping angle of 90° (vertical fractures), including hydraulic fractures; (2) fractures intersect the wellbore with dipping angles from 0° to less than 90°; (3) fractures are in the vicinity of the wellbore; (4) fractures have extremely high or low fracture and fault conductivities; (5) fractures have various sizes and distributions; (6) fractures have high and low matrix block permeabilities; and (7) fractures are damaged (skin zone) as a result of drilling and completion operations and fluids. All flow regimes associated with these factors are shown for a number of continuously and discretely fractured reservoirs with different well and fracture configurations. For a few cases, these flow regimes were compared with those from the field data. We performed history matching of the pressure-transient data generated from our discretely and continuously fractured reservoir models with the Warren and Root (1963) dual-porosity-type models, and it is shown that they yield incorrect reservoir parameters.

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An efficient method for fractured shale reservoir history matching: The embedded discrete fracture multi-continuum approach

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2017.10.055 ◽

2018 ◽

Vol 160 ◽

pp. 170-181 ◽

Cited By ~ 25

Author(s):

Z. Chai ◽

B. Yan ◽

J.E. Killough ◽

Y. Wang

Keyword(s):

Efficient Method ◽

History Matching ◽

Continuum Approach ◽

Discrete Fracture ◽

Reservoir History Matching ◽

Shale Reservoir

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Consistent Formulation and Error Statistics for Reservoir History Matching

10.3997/2214-4609.202035022 ◽

2020 ◽

Author(s):

G. Evensen

Keyword(s):

History Matching ◽

Error Statistics ◽

Reservoir History Matching ◽

Consistent Formulation

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Ensemble size investigation in adaptive ES-MDA reservoir history matching

Journal of the Brazilian Society of Mechanical Sciences and Engineering ◽

10.1007/s40430-019-1935-0 ◽

2019 ◽

Vol 41 (10) ◽

Author(s):

Paulo Henrique Ranazzi ◽

Marcio Augusto Sampaio

Keyword(s):

History Matching ◽

Ensemble Size ◽

Reservoir History Matching

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An Integrated Approach for History Matching of Multiscale-Fracture Reservoirs

SPE Journal ◽

10.2118/195589-pa ◽

2019 ◽

Vol 24 (04) ◽

pp. 1508-1525

Author(s):

Mengbi Yao ◽

Haibin Chang ◽

Xiang Li ◽

Dongxiao Zhang

Keyword(s):

History Matching ◽

Large Scale ◽

Fractured Reservoirs ◽

Integrated Approach ◽

Small Scale ◽

Gaussian Field ◽

Limited Information ◽

Fractured Reservoir ◽

Discrete Fracture Model ◽

Single Scale

Summary Naturally or hydraulically fractured reservoirs usually contain fractures at various scales. Among these fractures, large-scale fractures might strongly affect fluid flow, making them essential for production behavior. Areas with densely populated small-scale fractures might also affect the flow capacity of the region and contribute to production. However, because of limited information, locating each small-scale fracture individually is impossible. The coexistence of different fracture scales also constitutes a great challenge for history matching. In this work, an integrated approach is proposed to inverse model multiscale fractures hierarchically using dynamic production data. In the proposed method, a hybrid of an embedded discrete fracture model (EDFM) and a dual-porosity/dual-permeability (DPDP) model is devised to parameterize multiscale fractures. The large-scale fractures are explicitly modeled by EDFM with Hough-transform-based parameterization to maintain their geometrical details. For the area with densely populated small-scale fractures, a truncated Gaussian field is applied to capture its spatial distribution, and then the DPDP model is used to model this fracture area. After the parameterization, an iterative history-matching method is used to inversely model the flow in a fractured reservoir. Several synthetic cases, including one case with single-scale fractures and three cases with multiscale fractures, are designed to test the performance of the proposed approach.

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Using Bayesian Model Probability for Ranking Different Prior Scenarios in Reservoir History Matching

SPE Journal ◽

10.2118/194505-pa ◽

2019 ◽

Vol 24 (04) ◽

pp. 1490-1507 ◽

Cited By ~ 1

Author(s):

Sigurd Ivar Aanonsen ◽

Svenn Tveit ◽

Mathias Alerini

Keyword(s):

History Matching ◽

Fault Location ◽

Model Updating ◽

A Priori ◽

Gaussian Random Fields ◽

Model Probability ◽

Reservoir History Matching ◽

Reservoir Compartmentalization ◽

Alternative Scenarios ◽

Reservoir Simulation Model

Summary This paper considers Bayesian methods to discriminate between models depending on posterior model probability. When applying ensemble-based methods for model updating or history matching, the uncertainties in the parameters are typically assumed to be univariate Gaussian random fields. In reality, however, there often might be several alternative scenarios that are possible a priori. We take that into account by applying the concepts of model likelihood and model probability and suggest a method that uses importance sampling to estimate these quantities from the prior and posterior ensembles. In particular, we focus on the problem of conditioning a dynamic reservoir-simulation model to frequent 4D-seismic data (e.g., permanent-reservoir-monitoring data) by tuning the top reservoir surface given several alternative prior interpretations with uncertainty. However, the methodology can easily be applied to similar problems, such as fault location and reservoir compartmentalization. Although the estimated posterior model probabilities will be uncertain, the ranking of models according to estimated probabilities appears to be quite robust.

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