Identification of Geological Shapes in Reservoir Engineering By History Matching Production Data

1999 ◽  
Vol 2 (05) ◽  
pp. 470-477 ◽  
Author(s):  
Daniel Rahon ◽  
Paul Francis Edoa ◽  
Mohamed Masmoudi

Summary This paper discusses a method which helps identify the geometry of geological features in an oil reservoir by history matching of production data. Following an initial study on single-phase flow and applied to well tests (Rahon, D., Edoa, P. F., and Masmoudi, M.: "Inversion of Geological Shapes in Reservoir Engineering Using Well Tests and History Matching of Production Data," paper SPE 38656 presented at the 1997 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 5–8 October.), the research presented here was conducted in a multiphase flow context. This method provides information on the limits of a reservoir being explored, the position and size of faults, and the thickness and dimensions of channels. The approach consists in matching numerical flow simulation results with production measurements. This is achieved by modifying the geometry of the geological model. The identification of geometric parameters is based on the solution of an inverse problem and boils down to minimizing an objective function integrating the production data. The minimization algorithm is rendered very efficient by calculating the gradients of the objective function with respect to perturbations of these geometric parameters. This leads to a better characterization of the shape, the dimension, and the position of sedimentary bodies. Several examples are presented in this paper, in particular, an application of the method in a two-phase water/oil case. Introduction A number of semiautomatic history matching techniques have been developed in recent years to assist the reservoir engineer in his reservoir characterization task. These techniques are generally based on the resolution of an inverse problem by the minimization of an objective function and require the use of a numerical simulator. The matching parameters of the inverse problem comprise two types of properties: petrophysical/porosity and permeability and geometric position, shape, and size of the sedimentary bodies present in the reservoir. To be efficient, minimization algorithms require the calculation of simulated production gradients with respect to matching parameters. Such gradients are usually calculated by deriving discrete state equations solved in the numerical simulator1–5 or by using a so-called adjoint-state method.6,7 Therefore, most of these gradient-based methods only allow the identification of petrophysical parameters which appear explicitly in the discrete equations of state. The case of geometric parameters is much more complex, as the gradients of the objective function with respect to these parameters cannot be determined directly from the flow equation. Recent works8–10 have handled this problem by defining geological objects using mathematical functions to describe porosity or permeability fields. But, generalizing these solutions to complex geological models remains difficult. The method proposed in this paper is well suited to complex geometries and heterogeneous environments. The history matching parameters are the geometric elements that describe the geological objects generated, for example, with a geomodeling tool. A complete description of the method with the calculation of the sensitivities was presented in Ref. 11, within the particular framework of single-phase flow adapted to well-test interpretations. In this paper we will introduce an extension of the method to multiphase equations in order to match production data. Several examples are presented, illustrating the efficiency of this technique in a two-phase context. Description of the Method The objective is to develop an automatic or semiautomatic history matching method which allows identification of geometric parameters that describe geological shapes using a numerical simulator. To be efficient, the optimization process requires the calculation of objective function gradients with respect to the parameters. With usual fluid flow simulators using a regular grid or corner point geometry, the conventional methods for calculating well response gradients on discrete equations are not readily usable when dealing with geometric parameters. These geometric parameters do not appear explicitly in the model equations. With these kinds of structured models the solution is to determine the expression of the sensitivities of the objective function in the continuous problem using mathematical theory and then to calculate a discrete set of gradients. Sensitivity Calculation. Here, we present a sensitivity calculation to the displacement of a geological body in a two-phase water/oil flow context. State Equations. Let ? be a two- or three-dimensional spatial field, with a boundary ? and let ]0,T[ be the time interval covering the pressure history. We assume that the capillary pressure is negligible. The pressure p and the water saturation S corresponding to a two-phase flow in the domain ? are governed by the following equations: ∂ ϕ ( p ) S ∂ t − ∇ . ( k k r o ( S ) μ o ∇ ( p + ρ o g z ) ) = q o ρ o , ∂ ϕ ( p ) S ∂ t − ∇ . ( k k r w ( S ) μ w ∇ ( p + ρ w g z ) ) = q w ρ w , ( x , y , z ) ∈ Ω , t ∈ ] 0 , T [ , ( 1 ) with a no-flux boundary condition on ? and an initial equilibrium condition

SPE Journal ◽  
2017 ◽  
Vol 22 (05) ◽  
pp. 1506-1518 ◽  
Author(s):  
Pedram Mahzari ◽  
Mehran Sohrabi

Summary Three-phase flow in porous media during water-alternating-gas (WAG) injections and the associated cycle-dependent hysteresis have been subject of studies experimentally and theoretically. In spite of attempts to develop models and simulation methods for WAG injections and three-phase flow, current lack of a solid approach to handle hysteresis effects in simulating WAG-injection scenarios has resulted in misinterpretations of simulation outcomes in laboratory and field scales. In this work, by use of our improved methodology, the first cycle of the WAG experiments (first waterflood and the subsequent gasflood) was history matched to estimate the two-phase krs (oil/water and gas/oil). For subsequent cycles, pertinent parameters of the WAG hysteresis model are included in the automatic-history-matching process to reproduce all WAG cycles together. The results indicate that history matching the whole WAG experiment would lead to a significantly improved simulation outcome, which highlights the importance of two elements in evaluating WAG experiments: inclusion of the full WAG experiments in history matching and use of a more-representative set of two-phase krs, which was originated from our new methodology to estimate two-phase krs from the first cycle of a WAG experiment. Because WAG-related parameters should be able to model any three-phase flow irrespective of WAG scenarios, in another exercise, the tuned parameters obtained from a WAG experiment (starting with water) were used in a similar coreflood test (WAG starting with gas) to assess predictive capability for simulating three-phase flow in porous media. After identifying shortcomings of existing models, an improved methodology was used to history match multiple coreflood experiments simultaneously to estimate parameters that can reasonably capture processes taking place in WAG at different scenarios—that is, starting with water or gas. The comprehensive simulation study performed here would shed some light on a consolidated methodology to estimate saturation functions that can simulate WAG injections at different scenarios.


1980 ◽  
Vol 20 (06) ◽  
pp. 521-532 ◽  
Author(s):  
A.T. Watson ◽  
J.H. Seinfeld ◽  
G.R. Gavalas ◽  
P.T. Woo

Abstract An automatic history-matching algorithm based onan optimal control approach has been formulated forjoint estimation of spatially varying permeability andporosity and coefficients of relative permeabilityfunctions in two-phase reservoirs. The algorithm usespressure and production rate data simultaneously. The performance of the algorithm for thewaterflooding of one- and two-dimensional hypotheticalreservoirs is examined, and properties associatedwith the parameter estimation problem are discussed. Introduction There has been considerable interest in thedevelopment of automatic history-matchingalgorithms. Most of the published work to date onautomatic history matching has been devoted tosingle-phase reservoirs in which the unknownparameters to be estimated are often the reservoirporosity (or storage) and absolute permeability (ortransmissibility). In the single-phase problem, theobjective function usually consists of the deviationsbetween the predicted and measured reservoirpressures at the wells. Parameter estimation, orhistory matching, in multiphase reservoirs isfundamentally more difficult than in single-phasereservoirs. The multiphase equations are nonlinear, and in addition to the porosity and absolutepermeability, the relative permeabilities of each phasemay be unknown and subject to estimation. Measurements of the relative rates of flow of oil, water, and gas at the wells also may be available forthe objective function. The aspect of the reservoir history-matchingproblem that distinguishes it from other parameterestimation problems in science and engineering is thelarge dimensionality of both the system state and theunknown parameters. As a result of this largedimensionality, computational efficiency becomes aprime consideration in the implementation of anautomatic history-matching method. In all parameterestimation methods, a trade-off exists between theamount of computation performed per iteration andthe speed of convergence of the method. Animportant saving in computing time was realized insingle-phase automatic history matching through theintroduction of optimal control theory as a methodfor calculating the gradient of the objective functionwith respect to the unknown parameters. Thistechnique currently is limited to first-order gradientmethods. First-order gradient methods generallyconverge more slowly than those of higher order.Nevertheless, the amount of computation requiredper iteration is significantly less than that requiredfor higher-order optimization methods; thus, first-order methods are attractive for automatic historymatching. The optimal control algorithm forautomatic history matching has been shown toproduce excellent results when applied to field problems. Therefore, the first approach to thedevelopment of a general automatic history-matchingalgorithm for multiphase reservoirs wouldseem to proceed through the development of anoptimal control approach for calculating the gradientof the objective function with respect to theparameters for use in a first-order method. SPEJ P. 521^


2021 ◽  
Vol 2090 (1) ◽  
pp. 012139
Author(s):  
OA Shishkina ◽  
I M Indrupskiy

Abstract Inverse problem solution is an integral part of data interpretation for well testing in petroleum reservoirs. In case of two-phase well tests with water injection, forward problem is based on the multiphase flow model in porous media and solved numerically. The inverse problem is based on a misfit or likelihood objective function. Adjoint methods have proved robust and efficient for gradient calculation of the objective function in this type of problems. However, if time-lapse electrical resistivity measurements during the well test are included in the objective function, both the forward and inverse problems become multiphysical, and straightforward application of the adjoint method is problematic. In this paper we present a novel adjoint algorithm for the inverse problems considered. It takes into account the structure of cross dependencies between flow and electrical equations and variables, as well as specifics of the equations (mixed parabolic-hyperbolic for flow and elliptic for electricity), numerical discretizations and grids, and measurements in the inverse problem. Decomposition is proposed for the adjoint problem which makes possible step-wise solution of the electric adjoint equations, like in the forward problem, after which a cross-term is computed and added to the right-hand side of the flow adjoint equations at this timestep. The overall procedure provides accurate gradient calculation for the multiphysical objective function while preserving robustness and efficiency of the adjoint methods. Example cases of the adjoint gradient calculation are presented and compared to straightforward difference-based gradient calculation in terms of accuracy and efficiency.


2012 ◽  
Vol 326-328 ◽  
pp. 221-226
Author(s):  
Jozef Kačur ◽  
Benny Malengier ◽  
Pavol Kišon

Numerical modeling of two-phase flow under centrifugation is presented in 1D.A new method is analysed to determine capillary-pressure curves. This method is based onmodeling the interface between the zone containing only wetting liquid and the zone containingwetting and non wetting liquids. This interface appears when into a fully saturated sample withwetting liquid we inject a non-wetting liquid. By means of this interface an efficient and correctnumerical approximation is created based upon the solution of ODE and DAE systems. Bothliquids are assumed to be immiscible and incompressible. This method is a good candidate tobe used in solution of inverse problem. Some numerical experiments are presented.


Author(s):  
M S Mohseni ◽  
B Miripour-Fard ◽  
A Zajkani

A rotating helical pump is introduced in this article. This pump can transport gas—liquid two-phase flow. A prototype of the pump was manufactured, and its operational performance was determined experimentally. The influence of the pump geometric parameters on its performance was investigated. Dimensionless parameters were derived from the Buckingham π-theorem to find the effect of a wide range of parameters. Results provide some useful information about the performance of the rotating helical pump.


Author(s):  
Dae W. Kim ◽  
Emil Rahim ◽  
Avram Bar-Cohen ◽  
Bongtae Han

The thermofluid characteristics of a chip-scale microgap cooler, including single-phase flow of water and FC-72 and flow boiling of FC-72, are explored. Heat transfer and pressure drop results for single phase water are used to validate a detailed numerical model and, together with the convective FC-72 data, establish a baseline for microgap cooler performance. Experimental results for single phase water and FC-72 flowing in 120 μm, 260 μm and 600 μm microgap coolers, 31mm wide by 34mm long, at velocities of 0.1 – 2 m/s are reported. “Pseudo-boiling” driven by dissolved gas and flow boiling of FC-72 are found to provide significant enhancement in heat transfer relative to theoretical single phase values.


SPE Journal ◽  
2007 ◽  
Vol 12 (04) ◽  
pp. 408-419 ◽  
Author(s):  
Baoyan Li ◽  
Francois Friedmann

Summary History matching is an inverse problem in which an engineer calibrates key geological/fluid flow parameters by fitting a simulator's output to the real reservoir production history. It has no unique solution because of insufficient constraints. History-match solutions are obtained by searching for minima of an objective function below a preselected threshold value. Experimental design and response surface methodologies provide an efficient approach to build proxies of objective functions (OF) for history matching. The search for minima can then be easily performed on the proxies of OF as long as its accuracy is acceptable. In this paper, we first introduce a novel experimental design methodology for semi-automatically selecting the sampling points, which are used to improve the accuracy of constructed proxies of the nonlinear OF. This method is based on derivatives of constructed proxies. We propose an iterative procedure for history matching, applying this new design methodology. To obtain the global optima, the proxies of an objective function are initially constructed on the global parameter space. They are iteratively improved until adequate accuracy is achieved. We locate subspaces in the vicinity of the optima regions using a clustering technique to improve the accuracy of the reconstructed OF in these subspaces. We test this novel methodology and history-matching procedure with two waterflooded reservoir models. One model is the Imperial College fault model (Tavassoli et al. 2004). It contains a large bank of simulation runs. The other is a modified version of SPE9 (Killough 1995) benchmark problem. We demonstrate the efficiency of this newly developed history-matching technique. Introduction History matching (Eide et al. 1994; Landa and Güyagüler 2003) is an inverse problem in which an engineer calibrates key geological/fluid flow parameters of reservoirs by fitting a reservoir simulator's output to the real reservoir production history. It has no unique solution because of insufficient constraints. The traditional history matching is performed in a semi-empirical approach, which is based on the engineer's understanding of the field production behavior. Usually, the model parameters are adjusted using a one-factor-at-a-time approach. History matching can be very time consuming, because many simulation runs may be required for obtaining good fitting results. Attempts have been made to automate the history-matching process by using optimal control theory (Chen et al. 1974) and gradient techniques (Gomez et al. 2001). Also, design of experiment (DOE) and response surface methodologies (Eide et al. 1994; Box and Wilson 1987; Montgomery 2001; Box and Hunter 1957; Box and Wilson 1951; Damsleth et al. 1992; Egeland et al. 1992; Friedmann et al. 2003) (RSM) were introduced in the late 1990s to guide automatic history matching. The goal of these automatic methods is to achieve reasonably faster history-matching techniques than the traditional method. History matching is an optimization problem. The objective is to find the best of all possible sets of geological/fluid flow parameters to fit the production data of reservoirs. To assess the quality of the match, we define an OF (Atallah 1999). For history-matching problems, an objective function is usually defined as a distance (Landa and Güyagüler 2003) between a simulator's output and reservoir production data. History-matching solutions are obtained by searching for minima of the objective function. Experimental design and response surface methodologies provide an efficient approach to build up hypersurfaces (Kecman 2001) of objective functions (i.e., proxies of objective functions with a limited number of simulation runs for history matching). The search for minima can then be easily performed on these proxies as long as their accuracy is acceptable. The efficiency of this technique depends on constructing adequately accurate objective functions.


2013 ◽  
Vol 16 (04) ◽  
pp. 412-422
Author(s):  
A.M.. M. Farid ◽  
Ahmed H. El-Banbi ◽  
A.A.. A. Abdelwaly

Summary The depletion performance of gas/condensate reservoirs is highly influenced by changes in fluid composition below the dewpoint. The long-term prediction of condensate/gas reservoir behavior is therefore difficult because of the complexity of both composition variation and two-phase-flow effects. In this paper, an integrated model was developed to simulate gas-condensate reservoir/well behavior. The model couples the compositional material balance or the generalized material-balance equations for reservoir behavior, the two-phase pseudo integral pressure for near-wellbore behavior, and outflow correlations for wellbore behavior. An optimization algorithm was also used with the integrated model so it can be used in history-matching mode to estimate original gas in place (OGIP), original oil in place (OOIP), and productivity-index (PI) parameters for gas/condensate wells. The model also can be used to predict the production performance for variable tubinghead pressure (THP) and variable production rate. The model runs fast and requires minimal input. The developed model was validated by use of different simulation cases generated with a commercial compositional reservoir simulator for a variety of reservoir and well conditions. The results show a good agreement between the simulation cases and the integrated model. After validating the integrated model against the simulated cases, the model was used to analyze production data for a rich-gas/condensate field (initial condensate/gas ratio of 180 bbl/ MMscf). THP data for four wells were used along with basic reservoir and production data to obtain original fluids in place and PIs of the wells. The estimated parameters were then used to forecast the gas and condensate production above and below the dewpoint. The model is also capable of predicting reservoir pressure, bottomhole flowing pressure, and THP and can account for completion changes when they occur.


Sign in / Sign up

Export Citation Format

Share Document