Reservoir Management Under Geological Uncertainty Using Fast Model Update

Hanea, R.G.; Evensen, Geir; Hustoft, L.; Ek, T.; Chitu, Alin; Wilschut, F.

doi:10.2118/173305-ms

Cited by 35 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a reservoir history-matching problem, the model operator is typically the reservoir simulation model, which, given the input forcing and a parameterization, which defines the model, simulates the produced fluid rates. Some recent applications use a full Fast-Model-Update workflow [7,8] where the model workflow can also contain, e.g., a geological forward model, a reservoir simulation model, and a seismic forward model, which, given the inputs to the geological description, predicts the production of oil, water, gas, and the seismic response from the reservoir. Given a set of input parameters, x, the prediction of y is precisely determined by the model in Equation (1).…”

Section: History-matching Problemmentioning

confidence: 99%

Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching

Evensen

Raanes

Stordal

et al. 2019

Front. Appl. Math. Stat.

Self Cite

View full text Add to dashboard Cite

Raanes et al. [1] revised the iterative ensemble smoother of Chen and Oliver [2, 3], denoted Ensemble Randomized Maximum Likelihood (EnRML), using the property that the EnRML solution is contained in the ensemble subspace. They analyzed EnRML and demonstrated how to implement the method without the use of expensive pseudo inversions of the low-rank state covariance matrix or the ensemble-anomaly matrix. The new algorithm produces the same result, realization by realization, as the original EnRML method. However, the new formulation is simpler to implement, numerically stable, and computationally more efficient. The purpose of this document is to present a simple derivation of the new algorithm and demonstrate its practical implementation and use for reservoir history matching. An additional focus is to customize the algorithm to be suitable for big-data assimilation of measurements with correlated errors. The computational cost of the resulting "ensemble sub-space" algorithm is linear in both the dimension of the state space and the number of measurements, also when the measurements have correlated errors. The final algorithm is implemented in the Ensemble Reservoir Tool (ERT) for running and conditioning ensembles of reservoir models. Several verification experiments are presented.

show abstract

Section: History-matching Problemmentioning

confidence: 99%

Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching

Evensen

Raanes

Stordal

et al. 2019

Front. Appl. Math. Stat.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The predictive value of a properly history-matched model is never guaranteed (and, in fact, usually rather limited), but it is generally accepted that a geologically realistic model will have a higher chance of properly predicting the future reservoir behavior than an unrealistic one. A major trend in reservoir data assimilation is therefore to perform the data assimilation directly on the fine scale geological model, a technique sometimes referred to as 'big loop' history matching, and to extend the range of unknown parameters to include structural features; see, e.g., Seiler et al (2010) and Hanea et al (2015).…”

Section: Application Case Reservoir Engineeringmentioning

confidence: 99%

Recent Developments in Closed-Loop Approaches for Real-Time Mining and Petroleum Extraction

Benndorf

2017

Math Geosci

View full text Add to dashboard Cite

Advanced data acquisition and process modelling technology provide 'real-time' data and decision support capacity for different aspects of the resource extraction process. Closed-loop approaches have recently been applied to utilize information extracted from these data in combination with advanced computing technology for improved production control in mineral resource extraction. Similar techniques have been developed in the petroleum industry combining computer-assisted model updating with model-based production optimization. This contribution reviews recent developments and methods applied, highlights differences and assesses the potential value added for both application domains. The focus here is on the two main constituents of closed-loop concepts, data assimilation and optimization. Technological readiness of the constituents is assessed, and gaps for further technology development are identified. The value added is illustrated by means of selected cases. Key Wordsclosed-loop reservoir management, real-time mining, data assimilation, optimization Published as Benndorf, J. and Jansen, J.D., 2017: Recent developments in closed-loop approaches for realtime mining and petroleum extraction. Mathematical Geosciences 49 (3) 277-306.

show abstract

“…Despite much work performed in the area of data uncertainty management (Feraille and Busby 2009, Hanea et al 2015, Rijnders 1974, and Tipping et al 2006), many issues still exist regarding data management during the field development plan. The multivariate approach is based on multiple runs of all possible realizations of the initial data on the simulation model.…”

Section: The Concept Of Multivariate Approach In 3d Reservoir Simulationmentioning

confidence: 99%

Management of Initial Data Uncertainties in 3D Reservoir Simulation

Sadykov

Mikhaylov

2015

All Days

View full text Add to dashboard Cite

This paper demonstrates the multivariate approach as a tool for the management of initial data uncertainties in 3D reservoir simulation processes. The multivariate approach helps to address several problems, including optimizing the development scenario, determining the well type, and calculating the optimal horizontal well completion system.The method for determining the optimal development scenarios includes performing multiple forecast runs on simulation models with variations of initial data (such as permeability and water/oil contact) for various development scenarios (including well spacing and development plan). Then, the data are systematized, and a statistical analysis is performed with the calculation of economic indicators. The distributions of technical and economic development parameters are built from the results of these processes. The optimal development scenario is selected directly by selecting the realization with the maximum probability of occurrence from the most economically successful realizations. In other words, a development scenario is considered optimal if it has maximum probability of occurrence in the data set and maximum economic indicators (net present value (NPV) and profitability index (PI)).The paper uses two case studies to illustrate the multivariate approach for determining the optimal development scenarios by considering the initial data uncertainties.For gas field A (eastern Caspian, lower Jurassic terrigenous deposits), the task of determining the optimal well type and spacing of the new wells was addressed by using multivariate reservoir simulation. The data uncertainty is attributable to a core test result error in the permeability determination and to a lack of data for the gas/water contact depth in accordance with the well testing and production testing results. A total of 10,000 simulations of the multivariate reservoir and economic model were run to determine the optimal well type and well spacing. In addition, probabilistic values of the development technical and economic indicators P10, P50, and P90 were determined. The results of the calculation enabled the optimal development parameters to be selected and the effect of the initial data uncertainties on the final technical and economic indicators of the field development to be evaluated.The length of horizontal wells, hydraulic fracture spacing, and optimal well spacing were selected based on the multivariate approach for oilfield B (western Siberian oil and gas province, upper Jurassic). For this purpose, 100,000 iterations of the development scenarios forecast were calculated based on the multivariate simulation model. Based on the analysis of the calculations and economic model, the optimal length of the horizontal section and fracture spacing were selected and justified, enabling the optimal design and completion system for horizontal wells, as well as risk mitigation measures, to be selected.

show abstract

Reservoir Management Under Geological Uncertainty Using Fast Model Update

Cited by 35 publications

References 10 publications

Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching

Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching

Recent Developments in Closed-Loop Approaches for Real-Time Mining and Petroleum Extraction

Management of Initial Data Uncertainties in 3D Reservoir Simulation

Contact Info

Product

Resources

About