Appropriate formulation of the objective function for the history matching of seismic attributes

Tillier, E.; Veiga, Sébastien da; Derfoul, Ratiba

doi:10.1016/j.cageo.2012.07.031

Cited by 36 publications

(23 citation statements)

References 16 publications

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“…History matching is considered an inverse problem (Kretz et al, 2004, Tillier et al, 2012, it is a process of simultaneously perturbing reservoir parameters such that it can be represented as a minimization problem where observed dynamic data are used to condition reservoir models by reducing the misfit between the observed data and model predicted data through an objective function. The use of the conventional least squares formulation for computing production data objective function and misfit has been shown to be suitable and efficient (Oliver and Chen, 2011), such that it can be significantly reduced during the history matching process, and properly characterizes the error between the simulated data and the real data (Tillier et al, 2013); hence this approach is used in this work. However, applying the least squares formulation to compute the seismic objective function and mismatch has been shown to be unsuitable because of the nature of seismic data , Tillier et al, 2013, hence the need to search for a suitable alternative.…”

Section: Methodsmentioning

confidence: 99%

“…The use of the conventional least squares formulation for computing production data objective function and misfit has been shown to be suitable and efficient (Oliver and Chen, 2011), such that it can be significantly reduced during the history matching process, and properly characterizes the error between the simulated data and the real data (Tillier et al, 2013); hence this approach is used in this work. However, applying the least squares formulation to compute the seismic objective function and mismatch has been shown to be unsuitable because of the nature of seismic data , Tillier et al, 2013, hence the need to search for a suitable alternative.…”

Section: Methodsmentioning

confidence: 99%

“…The aforementioned domains use seismic modelling, rock physics modelling or petro-elastic modelling to address this challenge, however these modelling processes are complex, time consuming, uses laboratory stress sensitivity coefficients, as well as Gassmann's equation assumptions (Landrø, 2001, Stephen et al, 2005, Floricich, 2006, Wen et al, 2006, Amini, 2014. There have been other methods that circumvent the complex seismic modelling process (Landa and Horne, 1997, Kretz et al, 2004, Wen et al, 2006, Jin et al, 2012, Rukavishnikov and Kurelenkov, 2012, Tillier et al, 2013 which employed the use of image analysis tools, binary processing, or dynamic clusters to integrate the seismic data into the reservoir model. In this paper we propose a method where our seismic data and simulation data are converted to binary seismic gas maps and binary simulation gas maps respectively, such that we can compare the observed seismic data directly with the simulation output in the binary inversion domain (Figure 1).…”

Section: Introductionmentioning

confidence: 97%

See 2 more Smart Citations

Seismic Assisted History Matching Using Binary Image Matching

Obidegwu¹,

Chassagne²,

MacBeth³

2015

Europec 2015

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This paper presents a history matching scheme that has been applied to production data and time lapse seismic data. The production data objective function is calculated using the conventional least squares method between the historical production data and simulation predictions, while the seismic objective function uses the Hamming distance between two binary images of the gas distribution (presence of gas (1) or absence of gas (0)) sequenced over the different acquisition times. The technique is applied to a UKCS (United Kingdom Continental Shelf) field that has deep-water tertiary turbidite sands and multiple stacked reservoirs defining some degree of compartmentalisation. Thirty five parameters are perturbed in this history match, they can be classified as volumetric parameters (net-to-gross, pore volume), transmissibility parameters (permeability, transmissibility), and end points of the relative permeability curves (critical saturation points). An initial ensemble of fluid flow simulation models is created where the full range of uncertain parameters are acknowledged using experimental design methods, and an evolutionary algorithm is used for optimization in the history matching process. It is found that permeability and critical gas saturation are key parameters for achieving a good history match, and that the volumetric parameters are not significant for this match in this particular reservoir. We also observe that matching only to production data marginally improves the seismic match, whilst matching to only seismic data improves the fit to production data. Combining both sets of data delivers an improvement for the production data and seismic data, as well as an overall reduction in the uncertainties. A unique feature of this technique is the use of the Hamming distance metric for seismic data history matching analysis, as this circumvents the use of the uncertain petroelastic model. This approach is easy to implement, and also helps achieve an effective global history match.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Seismic Assisted History Matching Using Binary Image Matching

Obidegwu¹,

Chassagne²,

MacBeth³

2015

Europec 2015

View full text Add to dashboard Cite

show abstract

“…To address this problem, Tillier et al (2013) presented an appropriate objective function for history matching of seismic attributes based on image segmentation and a modified Hausdorff metric. The objective function of history matching commonly has a complex shape and multiple local minima (Oliver and Chen 2011).…”

Section: Automatic History Matchingmentioning

confidence: 99%

A review of closed-loop reservoir management

Hou

Zhou

Zhang³

et al. 2015

Pet. Sci.

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The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management: automatic history matching and reservoir production optimization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir management; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

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“…The proposed formulation originates from the "shape matching" research area, and is based on a Hausdorff distance. In the petroleum industry, the Hausdorff distance was mainly used to compute the dissimilarity between different images: for facies and petrophysical models (Suzuki and Caers, 2006) and for seismic data (Tillier et al, 2013;Abadpour et al, 2013). In this paper, we consider the use of Hausdorff distances to quantify the mismatch of production data, i.e., to compute the dissimilarity between curves.…”

Section: Introductionmentioning

confidence: 99%

A Better Formulation of Objective Functions for History Matching Using Hausdorff Distances

Bouzarkouna¹,

Nobakht

2015

Europec 2015

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History matching aims at finding one or several reservoir models that closely reproduce the past behavior of the reservoir. Objective functions are often used to measure the quality of these models.Indeed when using assisted history matching techniques, a good formulation of the objective function is crucial since it increases the likelihood to get an acceptable history match. Moreover, for manual history matching, and in particular for large fields, objective functions can also be used in order to compare between runs and to help understanding the field.In this paper, we investigate an alternative new formulation of the objective function. The proposed formulation does not take into account only the difference between the production data (point by point) but also discrepancies in the shape. The proposed formulation is based on an appropriate Hausdorff distance.The proposed approach is applied on different data from different real history matching studies. Results are compared with the standard mean squared error formulation, and shown to satisfy more reservoir engineering principles when comparing between runs.

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Appropriate formulation of the objective function for the history matching of seismic attributes

Cited by 36 publications

References 16 publications

Seismic Assisted History Matching Using Binary Image Matching

Seismic Assisted History Matching Using Binary Image Matching

A review of closed-loop reservoir management

A Better Formulation of Objective Functions for History Matching Using Hausdorff Distances

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