Direct forecasting of reservoir performance using production data without history matching

Satija, Addy; Scheidt, Céline; Li, Lewis; Caers, Jef

doi:10.1007/s10596-017-9614-7

Cited by 41 publications

(28 citation statements)

References 20 publications

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“…Satija and Caers [41] modified the PFA method using canonical functional component analysis to improve the linearity in the projected data. Satija and Caers [42] used the same approach in a reservoir problem and concluded that the method provided uncertainty estimates of production forecast in reasonable agreement with rejection sampling. More recently, He et al [22] used similar ideas from DSI to estimate the uncertainty reduction in a study to compute the value of information of data-acquisition plans.…”

Section: Introductionmentioning

confidence: 95%

Data-space inversion with ensemble smoother

2020

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Reservoir engineers use large-scale numerical models to predict the production performance in oil and gas fields. However, these models are constructed based on scarce and often inaccurate data, making their predictions highly uncertain. On the other hand, measurements of pressure and flow rates are constantly collected during the operation of the field. The assimilation of these data into the reservoir models (history matching) helps to mitigate uncertainty and improve their predictive capacity. History matching is a nonlinear inverse problem, which is typically handled using optimization and Monte Carlo methods. In practice, however, generating a set of properly history-matched models that preserve the geological realism is very challenging, especially in cases with complicated prior description, such as models with fractures and complex facies distributions. Recently, a new data-space inversion (DSI) approach was introduced in the literature as an alternative to the model-space inversion used in history matching. The essential idea is to update directly the predictions from a prior ensemble of models to account for the observed production history without updating the corresponding models. The present paper introduces a DSI implementation based on the use of an iterative ensemble smoother and demonstrates with examples that the new implementation is computationally faster and more robust than the earlier method based on principal component analysis. The new DSI is also applied to estimate the production forecast in a real field with long production history and a large number of wells. For this field problem, the new DSI obtained forecasts comparable with a more traditional ensemble-based history matching.

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Section: Introductionmentioning

confidence: 95%

Data-space inversion with ensemble smoother

2020

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“…We establish the geological uncertainty quantification framework based on BEL, which is briefly reviewed in this section. BEL is not a method, but a prescriptive and normative data-scientific protocol for designing uncertainty quantification within the context of decision making (Athens and Caers, 2019;Hermans et al, 2018;Scheidt et al, 2018). It integrates four constituents in UQ -data, model, prediction, and decision under the scientific methods and philosophy of Bayesianism.…”

Section: Overviewmentioning

confidence: 99%

“…The problem of geological uncertainty, due to its interpretative nature and the presence of prior information, is often handled in a Bayesian framework (Scheidt et al, 2018). The key part often lies in the joint quantification of the prior uncertainty on all modeling parameters, whether structural, lithological, petrophysical, or fluid.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, an uncertainty quantification protocol termed Bayesian evidential learning has been proposed to address decision making under uncertainty, and it has been applied to cases in oil or gas, groundwater contaminant remediation and geothermal energy (Athens and Caers, 2019;Hermans et al, 2018Hermans et al, , 2019Scheidt et al, 2018). It provides explicit standards that need to be reached at each stage of its UQ design with the purpose of decision making, including model falsification, global sensitivity analysis, prior elicitation, and data-science-driven uncertainty reduction under the principle of Bayesianism.…”

Section: Introductionmentioning

confidence: 99%

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Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

2020

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Abstract. Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data. We provide an automated method for uncertainty quantification and the updating of geological models using borehole data for subsurface developments within a Bayesian framework. Our methodologies are developed with the Bayesian evidential learning protocol for uncertainty quantification. Under such a framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics, and fluids), globally and spatially, without time-consuming model rebuilding. To address the above matters, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of a falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a statistical learning method that learns from a series of bijective operations to establish “Bayes–linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allow for fast-computation posterior geological models. The proposed framework is completely automated in an open-source project. We demonstrate its application by applying it to a generic gas reservoir dataset. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.

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“…1 The causal analysis approach involves estimating each cause from its effect (black arrows). The evidential analysis approach performs estimation by learning the relationship between data and target quantities (red arrow) [31,38]…”

Section: Introductionmentioning

confidence: 99%

Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties

Pradhan

Mukerji

2020

Comput Geosci

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We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties. The theoretical framework we develop allows incorporation of non-linear statistical models for seismic estimation problems. Uncertainty quantification is performed with Approximate Bayesian Computation. With the help of a synthetic example of estimation of reservoir net-to-gross and average fluid saturations in sub-resolution thin-sand reservoir, several nuances are foregrounded regarding the applicability of unsupervised and supervised learning methods for seismic estimation problems. Finally, we demonstrate the efficacy of our approach by estimating posterior uncertainty of reservoir net-to-gross in sub-resolution thin-sand reservoir from an offshore delta dataset using 3D pre-stack seismic data.

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Direct forecasting of reservoir performance using production data without history matching

Cited by 41 publications

References 20 publications

Data-space inversion with ensemble smoother

Data-space inversion with ensemble smoother

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties

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