History Matching and Predicting GOR Behavior With Analytical Functions

Sebelum suatu model reservoir digunakan, terlebih dahulu dilakukan history matching atau menyesuaikan kondisi model dengan dengan kondisi reservoir. Salah satu parameter yang perlu disesuaikan adalah permeabilitas relatif. Untuk melakukan rekonstruksi nilai permeabilitas relatifnya dibutuhkan data SCAL (Special Core Analysis) dari sampel core. Langkah awal rekonstruksi adalah dengan melakukan normalisasi data permeabilitas relatif (kr) dan saturasi air (Sw) dari data SCAL yang berasal dari tiga sampel core. Setelah dilakukan nomalisasi, dilakukan denormalisasi data permeabilitas relatif yang akan dikelompokkan berdasarkan jenis batuannya. Setelah dilakukan history matching menggunakan black oil simulator, data denormalisasi tersebut belum sesuai dengan kondisi aktual. Selanjutnya digunakan persamaan Corey untuk rekonstruksi kurva permeabilitas relatifnya. Hasil dari persamaan tersebut didapat bahwa nilai kro dan krw jenis batuan 1 sebesar 0,25 dan 0,09 kemudian nilai kro dan krw untuk jenis batuan 2 sebesar 0,4 dan 0,2. Nilai permeabilitas dari persamaan Corey digunakan untuk melakukan history matching, hasilnya didapat kecocokan (matching) dengan keadaan aktual. Berdasarkan hasil simulasi, nilai produksi minyak aktualnya adalah 1.465.650 bbl sedangkan produksi dari simulasi adalah 1.499.000 bbl. Artinya persentase perbandingan aktual dan simulasinya adalah 1,14% yang dapat dikatakan cocok karena persentase perbedaannya di bawah 5%.

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HISTORY MATCHING PRESSURE IN THE ARBUCKLE GROUP AQUIFER TO MANAGE MIDCONTINENT SEISMICITY

10.1130/abs/2019sc-326401 ◽

2019 ◽

Author(s):

Esmail Ansari ◽

◽

Tandis S. Bidgoli ◽

Andrew Michael Hollenbach

Keyword(s):

History Matching ◽

Arbuckle Group

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Future proofing a building design using history matching inspired level‐set techniques

Journal of the Royal Statistical Society Series C (Applied Statistics) ◽

10.1111/rssc.12461 ◽

2020 ◽

Author(s):

Evan Baker ◽

Peter Challenor ◽

Matt Eames

Keyword(s):

Level Set ◽

History Matching ◽

Building Design

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Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽

10.3390/en14041055 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1055

Author(s):

Qian Sun ◽

William Ampomah ◽

Junyu You ◽

Martha Cather ◽

Robert Balch

Keyword(s):

Machine Learning ◽

Oil Recovery ◽

History Matching ◽

Optimization Problems ◽

Learning Technologies ◽

Petroleum Engineering ◽

Support Vector ◽

Learning Approaches ◽

Field Development ◽

Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

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Efficient Dimensionality Reduction Methods in Reservoir History Matching

Energies ◽

10.3390/en14113137 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3137

Author(s):

Amine Tadjer ◽

Reider B. Bratvold ◽

Remus G. Hanea

Keyword(s):

Data Assimilation ◽

Dimensionality Reduction ◽

Gaussian Process ◽

Latent Variable ◽

History Matching ◽

Production Performance ◽

Latent Variable Model ◽

Variable Model ◽

Multiple Data ◽

Ensemble Smoother

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.

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