3D Field-Scale Automatic History Matching Using Adjoint Sensitivities and
Generalized Travel-Time Inversion

Daoud, Ahmed; Velasquez, Leonardo Vega

doi:10.2523/101779-ms

Cited by 3 publications

(10 citation statements)

References 18 publications

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“…The sensitivity computation using the adjoint method requires three major steps 7,8,9,10,17,21 : the forward model formulation, adjoint system of equations, and the sensitivity coefficient calculation. First, the forward model used is the commercial finite difference simulator ECLIPSE as it is robust and efficient for field-scale applications.…”

Section: Adjoint-methods Based Sensitivity For Three Phase Three Dimenmentioning

confidence: 99%

“…Second, the adjoint system of equations in complete discretized form 7,16,17,21 as given in Eqs. 1 to 3 is solved backward in time to obtain the adjoint variables at each grid block per each time step.…”

Section: Adjoint-methods Based Sensitivity For Three Phase Three Dimenmentioning

confidence: 99%

“…Different techniques are proposed to speed up the history matching process where all are grouped under what is called computer assisted or automatic history matching. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] The automatic history matching procedure involves the following steps; First, the forward model formulation, second, the data misfit calculation, third, the sensitivity coefficient calculation, and finally an optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Different ways are proposed to represent the data misfit; the amplitude misfit 7,8,9 , travel time 12 , and generalized travel time misfit. 10,[13][14][15][16][17] The generalized travel time is well suited for field-scale history matching as it considers all the observed data points and treat them as one objective function per well which is computationally efficient. 10,16,17 In addition, it has a linear convergence criteria compared to the amplitude misfit.…”

Section: Introductionmentioning

confidence: 99%

“…10,[13][14][15][16][17] The generalized travel time is well suited for field-scale history matching as it considers all the observed data points and treat them as one objective function per well which is computationally efficient. 10,16,17 In addition, it has a linear convergence criteria compared to the amplitude misfit. 19 Third, the sensitivity calculations are the main core of the history match procedure, and at the same time the most time consuming step in the process.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fast and Efficient Sensitivity Calculation Using Adjoint Method for 3 Phase Field-Scale History Matching

Azmy

Daoud

Abdlel-Fattah

et al. 2009

SPE Middle East Oil and Gas Show and Conference

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Adjoint method-based sensitivity for field-scale history matching with large number of parameters suffers from several limitations. First, the CPU time depends on the data points which are large for any brown fields of long history; second, it requires large memory to save the gridblock pressure and saturation per each time step used in the forward model. Third, it is computationally expensive as it requires solving the Adjoint system of equations backward in time per each forward time step which is usually of high magnitude in case of field scale applications of long history. Lastly, the solver used for solving the Adjoint system of equations needs to be efficient for large-scale applications. We propose an efficient and fast approach for sensitivity calculation based on the Adjoint method to overcome much of the current limitations. First, we use a commercial finite difference simulator, ECLIPSE, as a forward model, which is general and can account for complex physical behavior that dominates most field applications. Second, the production data misfit is represented by a single generalized travel time misfit per well, thus effectively reducing the number of data points into one per well. Third, we solve the Adjoint system of equations backward in time in larger time step that is equivalent to the time of severe changes in pressure and saturation due to the changing of well conditions or introducing new infill wells rather than using the forward model time steps. This approach reduces the computational effort and memory allocated for the sensitivity calculation. Fourth, we use an iterative sparse matrix solver, LSQR, for solving the Adjoint system of equations which shows high stability for field-scale applications. We demonstrate the power and utility of our approach using synthetic and pseudo field examples. The synthetic examples show the robustness and efficiency of our sensitivity calculation approach compared to the perturbation. The pseudo-field example has 10 years of production history with original gas cap and oil-water contact with strong aquifer support. Using well log data, core data, water-cut and gas-oil ratio history from producing wells; we characterize the permeability at each cell, thus demonstrating the feasibility of our approach for field applications.

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Section: Adjoint-methods Based Sensitivity For Three Phase Three Dimenmentioning

confidence: 99%

Section: Adjoint-methods Based Sensitivity For Three Phase Three Dimenmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fast and Efficient Sensitivity Calculation Using Adjoint Method for 3 Phase Field-Scale History Matching

Azmy

Daoud

Abdlel-Fattah

et al. 2009

SPE Middle East Oil and Gas Show and Conference

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Look‐ahead vertical seismic profiling inversion approach for density and compressional wave velocity in Bayesian framework

Daoud

Dayem

2019

Geophysical Prospecting

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To reduce drilling uncertainties, zero‐offset vertical seismic profiles can be inverted to quantify acoustic properties ahead of the bit. In this work, we propose an approach to invert vertical seismic profile corridor stacks in Bayesian framework for look‐ahead prediction. The implemented approach helps to successfully predict density and compressional wave velocity using prior knowledge from drilled interval. Hence, this information can be used to monitor reservoir depth as well as quantifying high‐pressure zones, which enables taking the correct decision during drilling. The inversion algorithm uses Gauss–Newton as an optimization tool, which requires the calculation of the sensitivity matrix of trace samples with respect to model parameters. Gauss–Newton has quadratic rate of convergence, which can speed up the inversion process. Moreover, geo‐statistical analysis has been used to efficiently utilize prior information supplied to the inversion process. The algorithm has been tested on synthetic and field cases. For the field case, a zero‐offset vertical seismic profile data taken from an offshore well were used as input to the inversion algorithm. Well logs acquired after drilling the prediction section was used to validate the inversion results. The results from the synthetic case applications were encouraging to accurately predict compressional wave velocity and density from just a constant prior model. The field case application shows the strength of our proposed approach in inverting vertical seismic profile data to obtain density and compressional wave velocity ahead of a bit with reasonable accuracy. Unlike the commonly used vertical seismic profile inversion approach for acoustic impedance using simple error to represent the prior covariance matrix, this work shows the importance of inverting for both density and compressional wave velocity using geo‐statistical knowledge of density and compressional wave velocity from the drilled section to quantify the prior covariance matrix required during Bayesian inversion.

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Streamline-Based History Matching of Arrival Times and Bottomhole Pressure Data for Multicomponent Compositional Systems

Tanaka

Kam

Datta‐Gupta

et al. 2015

SPE Annual Technical Conference and Exhibition

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Streamline-based history matching techniques have provided significant capabilities in integrating field-scale water-cut and tracer data into high resolution geologic models. The effectiveness of the streamline approach lies in the fact that parameter sensitivities can be computed analytically as one-dimensional integrals along streamlines and requires little additional computational overhead beyond the forward simulation. However, application of the streamline-based approach for simultaneous integration of water-cut and bottomhole pressure has been rather limited. This is partly because the convective streamlines appear to offer no particular advantage while computing parameter sensitivities for the bottomhole pressure data. This limits the utility of streamline-based history matching particularly for three-phase black-oil and compositional systems where the integration of pressure data is a requirement to accurately model reservoir depletion mechanisms. In this paper we first introduce a novel semi-analytic approach to compute the sensitivity of the bottomhole pressure data with respect to grid block properties. The approach takes advantage of the streamline trajectories and yields results that are comparable to sensitivities computed from adjoint methods, at a fraction of the computational cost. The bottomhole pressure sensitivities can be easily integrated with the water-cut, gas oil ratio or composition sensitivities for a joint inversion of pressure data using high resolution geologic models. An iterative least squared method (LSQR) is used to minimize a penalized objective function that includes the data misfit and appropriate ‘norm’ and ‘roughness’ penalty terms to preserve the prior model characteristics during the inversion. The proposed approach is well suited for both streamline and finite difference methods with access to streamlines, and has been generalized for application to three-phase and compositional systems by integrating water-cut, gas oil ratio, and bottomhole pressure data. We demonstrate the power and utility of our proposed method using synthetic and field examples. The synthetic examples include black-oil and compositional cases involving gas injection. The streamline-based sensitivities are compared with adjoint methods for verification purposes. We then apply the method to the Brugge benchmark case and the Norne field to demonstrate the practical feasibility of the proposed method.

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3D Field-Scale Automatic History Matching Using Adjoint Sensitivities and Generalized Travel-Time Inversion

Cited by 3 publications

References 18 publications

Fast and Efficient Sensitivity Calculation Using Adjoint Method for 3 Phase Field-Scale History Matching

Fast and Efficient Sensitivity Calculation Using Adjoint Method for 3 Phase Field-Scale History Matching

Look‐ahead vertical seismic profiling inversion approach for density and compressional wave velocity in Bayesian framework

Streamline-Based History Matching of Arrival Times and Bottomhole Pressure Data for Multicomponent Compositional Systems

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