Papers by Mohsen Dadashpour
Reservoir Characterization Using Production Data and Time-Lapse Seismic Data
The most commonly encountered, and probably the most challenging task in reservoir engineering, i... more The most commonly encountered, and probably the most challenging task in reservoir engineering, is to describe the reservoir accurately and efficiently. An accurate description of a reservoir is cr ...

Journal of Petroleum Science and Engineering, Feb 1, 2014
Recently production optimization has achieved increasing attention in upstream petroleum industry... more Recently production optimization has achieved increasing attention in upstream petroleum industry. Here, we evaluate derivative free optimization methods for determination of the optimal production strategy using a numerical reservoir model which was prepared for a comparative study at the SPE Applied Technology Workshop in Brugge, June 2008. The pattern search Hooke-Jeeves, the reflection simplex Nelder-Mead, a new line-search derivative-free and a generalized pattern search methods are applied to the optimization problem. The line-search derivative-free algorithm is developed based on the existing line-search derivative free algorithms in combination with the Hooke-Jeeves pattern search method. The derivative free optimization results are compared with a gradient based sequential quadratic programming algorithm, but we clearly identify some issues limiting the performance of gradient based algorithms. In real applications our optimization problem is facing very costly function evaluations and at the same time one might have limitations in the computational budget. Therefore we are interested in methods that can improve the objective function with few function evaluations. The line-search derivative-free method performs more efficient and better than the other optimization methods. Ranking among the other four methods is somewhat more difficult, except that the Nelder-Mead method clearly has the slowest performance among these methods. We also observed that optimization with sequential quadratic programming had a high risk of getting trapped in a local optimum, which could be explained by properties of the objective function.
Journal of Geophysics and Engineering, Nov 28, 2007
Journal of Geophysics and Engineering, Feb 1, 2016

Porosity and Permeability Estimation by Gradient Based History Matching using Time-Lapse Seismic Data
All Days, Mar 11, 2007
A method based on the Gauss-Newton optimization technique for continuous model updating with resp... more A method based on the Gauss-Newton optimization technique for continuous model updating with respect to 4D seismic data is presented. The study uses a commercial finite difference black oil reservoir simulator and a standard rock physics model to predict seismic amplitudes as a function of porosity and permeabilities. The main objective of the study is to test the feasibility of using 4D seismic data as input to reservoir parameter estimation problems.The algorithm written for this study, which was initially developed for the estimation of saturation and pressure changes from time-lapse seismic data, consists of three parts: the reservoir simulator, the rock physics petro-elastic model, and the optimization algorithm. The time-lapse seismic data are used for observation purposes. In our example, a simulation model generated the seismic data, then the model was modified after this the algorithm was used to fit the data generated in the previous step.History matching of reservoir behavior is difficult because of the problem is not unique. More than one solution exists that matches the available data. Therefore, empirical knowledge about rock types from laboratory measurements are used to constraint the inversion process.The Gauss-Newton inversion reduces the misfit between observed and calculated time-lapse seismic amplitudes. With this method, it is possible to estimate porosity and permeability distributions from time-lapse data. Since these parameters are estimated for every single grid cell in the reservoir model, the number of model parameters is high, and therefore the problem will be underdetermined. Therefore, a good fit with the observation data is not necessary for a good estimation of the unknown reservoir properties. The methods for reducing the number of unknown parameters and the associated uncertainties is discussed.

Journal of Geophysics and Engineering, Sep 10, 2010
In this study, we apply a derivative-free optimization algorithm to estimate porosity and permeab... more In this study, we apply a derivative-free optimization algorithm to estimate porosity and permeability from time-lapse seismic data and production data from a real reservoir (Norne field). In some circumstances, obtaining gradient information (exact and/or approximate) can be problematic e.g. derivatives are not available from a commercial simulator, or results are needed within a very short time frame. Derivative-free optimization approaches can be very time consuming because they often require many simulations. Typically, one iteration roughly needs as many simulations as the number of optimization variables. In this work, we propose two ways to significantly increase the efficiency of an optimization methodology in model inversion problems. First, by principal component analysis we decrease the number of optimization variables while keeping geostatistical consistency, and second, noticing that some optimization methods are very amenable to being parallelized, we apply them within a distributed computing framework. If we combine all this, the model inversion approach can be robust, fairly efficient and very simple to implement. In this paper, we apply the methodology to two cases: a semi-synthetic model with noisy data, and a case based entirely on field data. The results show that the derivative-free approach presented is robust against noise in the data.
A case study of reservoir parameter estimation in Norne oil field, Norway by using Ensemble Kalman Filter (EnKF)
Elsevier eBooks, 2022
Experimental investigation of oil recovery during water imbibition
Journal of Petroleum Science and Engineering, 2006
Capillary imbibition and gravity are the main forces acting in fractured reservoirs. The cores us... more Capillary imbibition and gravity are the main forces acting in fractured reservoirs. The cores used in the laboratory are usually short while experimental investigation of the gravity forces requires long samples. Therefore an experimental study has been carried out on a long core with the length of 116 cm surrounded with a simulated fracture. Kerosene and a synthetic oil with a
Nonlinear inversion for estimating reservoir parameters from time-lapse seismic data
A case study of reservoir parameter estimation in Norne oil field, Norway by using Ensemble Kalman Filter (EnKF)
Innovative Exploration Methods for Minerals, Oil, Gas, and Groundwater for Sustainable Development, 2022

Journal of Geophysics and Engineering, 2008
Saturation and pore pressure changes within a reservoir can be estimated by a history matching pr... more Saturation and pore pressure changes within a reservoir can be estimated by a history matching process based on production data. If time-lapse seismic data are available, the same parameters might be estimated directly from the seismic data as well. There are several ways to combine these data sources for estimating these reservoir parameters. In this work, we formulate a nonlinear inversion scheme to estimate pressure and saturation changes from time-lapse seismic data. We believe that such a formulation will enable us to include seismic data in the reservoir simulator in an efficient way, by including a second term in the leastsquares objective function. A nonlinear Gauss-Newton inversion method is tested on a 2D synthetic dataset inspired by a field offshore from Norway. A conventional reservoir simulator has been used to produce saturation and pore pressure changes as a function of production time. A rock physics model converts these data into synthetic time-lapse seismic data. Finally, the synthetic time-lapse data are used to test the derived inversion algorithm. We find that the inversion results are strongly dependent on the input model, and this is expected since we are dealing with an ill-posed inversion problem. Since we estimate pressure and saturation change for each grid cell in the reservoir model, the number of model parameters is high, and therefore the problem is undetermined. From testing, using this particular dataset, we assume neither pressure nor saturation changes for the initial model. Although uncertainties associated with the proposed method are high, we think this might be a useful tool, since there are ways to reduce the number of model parameters and constrain the objective function by including production data and reservoir simulation data into this algorithm.

Journal of Geophysics and Engineering, 2009
This study presents a method based on the Gauss-Newton optimization technique for continuous rese... more This study presents a method based on the Gauss-Newton optimization technique for continuous reservoir model updating with respect to production history and time-lapse seismic data in the form of zero offset amplitudes and amplitude versus offset (AVO) gradients. The main objective of the study is to test the feasibility of using these integrated data as input to reservoir parameter estimation problems. Using only production data or zero offset time-lapse seismic amplitudes as observation data in the parameter estimation process cannot properly limit the solution space. The emphasis of this work is to use the integrated data combined with empirical knowledge about rock types from laboratory measurements, to further constrain the inversion process. The algorithm written for this study consists of three parts: the reservoir simulator, the rock physics petro-elastic model and the optimization algorithm. The Gauss-Newton inversion is tested at a 2D semi-synthetic model inspired by real field data from offshore Norway. The algorithm reduces the misfit between the observed and simulated data which make it possible to estimate porosity and permeability distributions. The Gauss-Newton optimization technique is an efficient parameter estimation technique. However, the numerical estimation of the gradient is time consuming, and it can be prohibitive for practical applications. This method is suitable for distributed computing which considerably reduces the total optimization time. The amount of reduction depends mainly on the number of available processors.
The Norne Field Case-A Unique Comparative Case Study
SPE Intelligent Energy …, 2010
Copyright 2010, Society of Petroleum Engineers This paper was prepared for presentation at the SP... more Copyright 2010, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Intelligent Energy Conference and Exhibition held in Utrecht, The Netherlands, 2325 March 2010. This paper was selected for presentation by an SPE program committee ...

Applied Mathematics and Computation, 2008
In many countries the power systems are going to move toward creating a competitive structure for... more In many countries the power systems are going to move toward creating a competitive structure for selling and buying electrical energy. These changes and the numerous advantages of the distributed generation units (DGs) in term of their technology enhancement and economical considerations have created more incentives to use these kinds of generators than before. Therefore, it is necessary to study the impact of DGs on the power systems, especially on the distribution networks. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering DGs. The main objective of the DFR is to minimize the deviation of the bus voltage, the number of switching operations and the total cost of the active power generated by DGs and distribution companies. Since the DFR is a nonlinear optimization problem, we apply the particle swarm optimization (PSO) approach to solve it. The feasibility of the proposed approach is demonstrated and compared with other evolutionary methods such as genetic algorithm (GA), Tabu search (TS) and differential evolution (DE) over a realistic distribution test system.
Reservoir Characterization Using Production Data and Time-Lapse Seismic Data
The most commonly encountered, and probably the most challenging task in reservoir engineering, i... more The most commonly encountered, and probably the most challenging task in reservoir engineering, is to describe the reservoir accurately and efficiently. An accurate description of a reservoir is cr ...
Integrating 4D seismic and production data for assisted history matching using principal component analysis
A Derivative-Free Approach to the Inverse Modelling of an Oil Reservoir
Porosity and permeability estimation using 4D seismic and production data
A Derivative-Free Approach to the Inverse Modeling of an Oil Reservoir
Porosity and Permeability Estimation from Time-Lapse Seismic Data
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Papers by Mohsen Dadashpour