Time-Dependent Proxy Modeling of SAGD Process

Fedutenko, Eugene; Yang, Chaodong; Card, Colin; Nghiem, Long X.

doi:10.2118/165395-ms

Cited by 13 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This type of proxy can approximate different areas in the subsurface or surface environment such as production optimization [100,167], uncertainty quantification [168,169], history matching [170,171], field development planning [172], risk analysis [173,174], gas lift optimization [109,175], gas storage management [176], screening purposes in fractured reservoirs [177], hydraulic fracturing [178], assessing the petrophysical and geomechanical properties of shale reservoirs [179], waterflooding optimization [180][181][182][183], well placement optimization [184][185][186], wellhead data interpretation [187], and well control optimization [188]. Additionally, TPMs have a wide range of applications in various EOR recovery techniques such as steam-assisted gravity drainage (SAGD) [189], CO 2 -gas-assisted gravity drainage (GAGD) [190], water alternating gas (WAG) [191,192], and chemical flooding [193].…”

Section: Traditional Proxy Models (Tpm)mentioning

confidence: 99%

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

View full text Add to dashboard Cite

Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.

show abstract

Section: Traditional Proxy Models (Tpm)mentioning

confidence: 99%

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

View full text Add to dashboard Cite

show abstract

“…3 The proxy modeling optimization has not been found in the literature when it comes to the CO 2 -GAGD technique. However, the proxy model has been implemented in different reservoir studies and EOR modeling, such as oil production optimization, 4,5 waterflooding processes, [6][7][8] gas flooding processes, 9 steam injection, [10][11][12][13] chemical flooding, 14 and history matching. [15][16][17] The DoE concepts generate various computer trials (realizations) for the subject by connecting the levels for every parameter.…”

Section: Introductionmentioning

confidence: 99%

“…The most common DoE approaches are fractional factorial design, 12 Central composite (CC) designs, 18,19 Doptimality also noun as D optimal design, 14 and Latin Hypercube Design. 15 There are many successful examples of using the proxy models in the literature of reservoir studies, for instance, the second-degree polynomial equation, 2,11,20,21 kriging algorithms, 11,15,22 and artificial neural networks algorithm. 5,15,23 Constructing a large and complex compositional reservoir flow simulation for the evaluation and optimization of the CO 2 -EOR, especially in a real oil field, is an expensive and time-consuming step, especially in the need to run hundreds of simulation scenarios through the optimization process.…”

Section: Introductionmentioning

confidence: 99%

“…The most common DoE approaches are fractional factorial design, 12 Central composite (CC) designs, 18,19 D‐optimality also noun as D optimal design, 14 and Latin Hypercube Design 15 . There are many successful examples of using the proxy models in the literature of reservoir studies, for instance, the second‐degree polynomial equation, 2,11,20,21 kriging algorithms, 11,15,22 and artificial neural networks algorithm 5,15,23 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Al‐Mudhafar

Rao

Srinivasan

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

The carbon dioxide (CO2) injection in oil reservoirs is a promising enhanced oil recovery method to reduce carbon emissions into the atmosphere. The CO2 injection simulation and optimization require a large computation time, especially in real large‐scale oil reservoirs. This paper integrates experimental design and machine learning to construct a reduced‐physics surrogate model alternative to the complex reservoir simulator. This surrogate model was used to evaluate and optimize the CO2‐assisted gravity drainage (CO2‐GAGD) process, applied to a clastic reservoir in the Rumaila oil field. In the GAGD process optimization, five operational decision parameters controlling the production and injection activities were manipulated to attain optimal future oil production. Hundreds of simulation runs were created by Latin Hypercube Sampling to build a proxy‐based optimization. The optimal scenario increased the cumulative oil production by 416 MMSTB at the end of 10‐year prediction period. Finally, four machine learning (ML) algorithms were built as proxy surrogates alternative to the complex compositional reservoir simulations: Quadratic Equation (QM), FUzzy logic‐GEnetic algorithm (FUzzy‐GEnetic), Multivariate Additive Regression Splines (MARS), and Generalized Boosted Modeling (GBM). To show how robust using the ML approaches for the fast CO2‐GAGD process optimization, the random subsampling cross‐validation was adopted to conclude the optimum proxy metamodel that provides the lowest mismatch between the proxy‐ and simulator‐based cumulative oil production. The GBM algorithm achieved the highest adjusted‐R2 (0.9973) and lowest root mean square prediction error MathClass-open( 3.704 × 1 0 6 MathClass-close) $(3.704\times 1{0}^{6})$ to produce the most accurate metamodel. The resulting accurate GBM proxy metamodel can be used for fast optimization of the CO2‐GAGD process. Specifically, GBM‐metamodel should lead to achieving higher optimal reservoir flow responses, especially with running a large number of simulation runs. Consequently, this proposed trained GBM‐ML proxy metamodel could be used for gas injection optimization and uncertainty assessment with far‐less computational effort than with the conventional approaches, which use a complex flow simulator.

show abstract

“…The computational time of proxy construction and evaluation can be then at the same order of magnitude as the actual simulation time which defies the very purpose of proxy as being a quick estimator. In our previous work(Fedutenko et al, 2013) we introduced the space-time separability hypothesis based on 1D cubic spline interpolation in the time domain. While providing an accurate prediction, this model however requires a fine sampling of the time series which, in turn, results in extra computational time.…”

mentioning

confidence: 99%

Time-Dependent Neural Network Based Proxy Modeling of SAGD Process

Fedutenko¹,

Yang²,

Card³

et al. 2014

Day 2 Wed, June 11, 2014

Self Cite

View full text Add to dashboard Cite

The present study proposes a novel single-layer Neural Network proxy to efficiently predict the production performance of oil reservoirs from a limited number of reservoir simulations. The proposed model is shown to provide powerful means for learning reservoir's dynamics from input-output relationships that is defined by multiple combinations of inputs and controls. A SAGD case with 3 well pairs is used to illustrate the approach. The workflow is organized as follows:1. Different numbers (from 30 to 200) of direct numerical simulations are conducted for the time horizon of ten years for the given reservoir. These simulations correspond to different combinations of the operational parameters sampled according to the Latin Hypercube Experimental Design (LHD). 2. The time series of the simulated entire field production performance (particularly: Cumulative Oil Production, cumulative SOR, Oil Recovery Factor, and Oil Production Rate) are used to build the corresponding Radial Basis Function (RBF) Network proxy model which represents production performance as objective functions of the operational parameters and time. The nodal Radial Functions of the Network are defined to exactly match the simulator's training outputs. The big advantage of this model is a combination of low computational complexity with high prediction accuracy. 3. The proxy model is then used to predict the production data for the given reservoir for any time period (within 10 training years) and any combination of the operational parameters. 4. The predicted data are compared with the actual simulation results for the same time period and the same combinations of the operational parameters to evaluate the prediction quality. It is shown that the proposed RBF proxy model can be used as a light version of simulator to estimate the production results for any combination of operational parameters and time horizon with much less computational efforts. The proposed approach is shown to provide a very efficient forecasting mechanism for the reservoir considered. The difference between the predicted and actual data could be as low as a few percent for the majority of the operational parameters depending on the number of simulations used to train the RBF model. In general, the accuracy of the proxy model increases with the number of training simulations.

show abstract

Time-Dependent Proxy Modeling of SAGD Process

Cited by 13 publications

References 6 publications

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Time-Dependent Neural Network Based Proxy Modeling of SAGD Process

Contact Info

Product

Resources

About