Experiences With Streamline-Based Three-Phase History Matching

2009 ◽  
Vol 12 (04) ◽  
pp. 528-541 ◽  
Author(s):  
Adedayo Oyerinde ◽  
Akhil Datta-Gupta ◽  
William J. Milliken

Summary Streamline-based assisted and automatic history matching techniques have shown great potential in reconciling high resolution geologic models to production data. However, a major drawback of these approaches has been incompressibility or slight compressibility assumptions that have limited applications to two-phase water/oil displacements only. Recent generalization of streamline models to compressible flow has greatly expanded the scope and applicability of streamline-based history matching, in particular for three-phase flow. In our previous work, we calibrated geologic models to production data by matching the water cut (WCT) and gas/oil ratio (GOR) using the generalized travel-time inversion (GTTI) technique. For field applications, however, the highly nonmonotonic profile of the GOR data often presents a challenge to this technique. In this work we present a transformation of the field production data that makes it more amenable to GTTI. Further, we generalize the approach to incorporate bottomhole flowing pressure during three-phase history matching. We examine the practical feasibility of the method using a field-scale synthetic example (SPE-9 comparative study) and a field application. The field case is a highly faulted, west-African reservoir with an underlying aquifer. The reservoir is produced under depletion with three producers, and over thirty years of production history. The simulation model has several pressure/volume/temperature (PVT) and special core analysis (SCAL) regions and more than 100,000 cells. The GTTI is shown to be robust because of its quasilinear properties as demonstrated by the WCT and GOR match for a period of 30 years of production history.

SPE Journal ◽  
2007 ◽  
Vol 12 (04) ◽  
pp. 475-485 ◽  
Author(s):  
Hao Cheng ◽  
Adedayo Stephen Oyerinde ◽  
Akhil Datta-Gupta ◽  
William J. Milliken

Summary Reconciling high-resolution geologic models to field production history is still by far the most time-consuming aspect of the workflow for both geoscientists and engineers. Recently, streamline-based assisted and automatic history-matching techniques have shown great potential in this regard, and several field applications have demonstrated the feasibility of the approach. However, most of these applications have been limited to two-phase water/oil flow under incompressible or slightly compressible conditions. We propose an approach to history matching three-phase flow using a novel compressible streamline formulation and streamline-derived analytic sensitivities. First, we use a generalized streamline model to account for compressible flow by introducing an "effective density" of total fluids along streamlines. This density term rigorously captures changes in fluid volumes with pressure and is easily traced along streamlines. A density-dependent source term in the saturation equation further accounts for the pressure effects during saturation calculations along streamlines. Our approach preserves the 1D nature of the saturation equation and all the associated advantages of the streamline approach with only minor modifications to existing streamline models. Second, we analytically compute parameter sensitivities that define the relationship between the reservoir properties and the production response, viz. water-cut and gas/oil ratio (GOR). These sensitivities are an integral part of history matching, and streamline models permit efficient computation of these sensitivities through a single flow simulation. Finally, for history matching, we use a generalized travel-time inversion that has been shown to be robust because of its quasilinear properties and converges in only a few iterations. The approach is very fast and avoids much of the subjective judgment and time-consuming trial-and-error inherent in manual history matching. We demonstrate the power and utility of our approach using both synthetic and field-scale examples. The synthetic case is used to validate our method. It entails the joint integration of water cut and gas/oil ratios (GORs) from a nine-spot pattern in reconstructing a reference permeability field. The field-scale example is a modified version of the ninth SPE comparative study and consists of 25 producers, 1 injector, and aquifer influx. Starting with a prior geologic model, we integrate water-cut and GOR history by the generalized travel-time inversion. Our approach is very fast and preserves the geologic continuity. Introduction Integration of production data typically requires the minimization of a predefined data misfit and penalty terms to match the observed and calculated production response (Oliver 1994; Vasco et al. 1999; Datta-Gupta et al. 2001; Reis et al. 2000; Landa et al. 1996; Anterion et al. 1989; Wu et al. 1999; Wang and Kovscek 2000; Sahni and Horne 2005). There are several approaches to such minimization, and these can be broadly classified into three categories: gradient-based methods, sensitivity-based methods, and derivative-free methods (Oliver 1994). The derivative-free approaches such as simulated annealing and genetic algorithm require numerous flow simulations and can be computationally prohibitive for field-scale applications with very large numbers of parameters. Gradient-based methods have been widely used for automatic history matching, although the rate of convergence of these methods is typically slower than that of the sensitivity-based methods, such as the Gauss-Newton or the LSQR method (Vega et al. 2004). An integral part of the sensitivity-based methods is the computation of sensitivity coefficients. There are several approaches to calculating sensitivity coefficients, and these generally fall into one of the three following categories: perturbation method, direct method, and adjoint state methods. The perturbation approach is the simplest and requires the fewest changes to an existing code. This approach requires (N+1) forward simulations, where N is the number of parameters. Obviously, this can be computationally prohibitive for reservoir models with many parameters. In the direct, or sensitivity-equation, method, the flow and transport equations are differentiated to obtain expressions for the sensitivity coefficients (Vasco et al. 1999). Because there is one equation for each parameter, this approach can require the same amount of work. A variation of this method, called the gradient simulator method, utilizes the discretized version of the flow equations and takes advantage of the fact that the coefficient matrix remains unchanged for all parameters and needs to be decomposed only once (Anterion et al. 1989). Thus, sensitivity computation for each parameter now requires a matrix-vector multiplication. This method obviously represents a significant improvement, but still can be computationally demanding for large number of parameters. Finally, the adjoint-state method requires derivation and solution of adjoint equations that can be significantly smaller in number compared to the sensitivity equations. The adjoint equations are obtained by minimizing the production data misfit with flow equations as constraint, and the implementation of the method can be quite complex and cumbersome for multiphase flow applications (Wu et al. 1999). Furthermore, the number of adjoint solutions will generally depend on the amount of production data and thus can be restrictive for field-scale applications.


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.


2020 ◽  
Vol 17 (5) ◽  
pp. 1370-1388
Author(s):  
Zhi-Gang Zhang ◽  
Yan-Bao Liu ◽  
Hai-Tao Sun ◽  
Wei Xiong ◽  
Kai Shen ◽  
...  

Abstract Nowadays, the unconventional gas-bearing system plays an increasingly important role in energy market. The performances of the current history-matching techniques are not satisfied when applied to such systems. To overcome this shortfall, an alternative approach was developed and applied to investigate production data from an unconventional gas-bearing system. In this approach, the fluid flow curve obtained from the field is the superposition of a series of Gaussian functions. An automatic computing program was developed in the MATLAB, and both gas and water field data collected from a vertical well in the Linxing Block, Ordos Basin, were used to present the data processing technique. In the reservoir study, the automatic computing program was applied to match the production data from a single coal seam, multiple coal seams and multiple vertically stacked reservoirs with favourable fitting results. Compared with previous approaches, the proposed approach yields better results for both gas and water production data and can calculate the contributions from different reservoirs. The start time of the extraction for each gas-containing unit can also be determined. The new approach can be applied to the field data prediction and designation for the well locations and patterns at the reservoir scale.


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 ◽  
2020 ◽  
Vol 25 (06) ◽  
pp. 3265-3279
Author(s):  
Hamidreza Hamdi ◽  
Hamid Behmanesh ◽  
Christopher R. Clarkson

Summary Rate-transient analysis (RTA) is a useful reservoir/hydraulic fracture characterization method that can be applied to multifractured horizontal wells (MFHWs) producing from low-permeability (tight) and shale reservoirs. In this paper, we applied a recently developed three-phase RTA technique to the analysis of production data from an MFHW completed in a low-permeability volatile oil reservoir in the Western Canadian Sedimentary Basin. This RTA technique is used to analyze the transient linear flow regime for wells operated under constant flowing bottomhole pressure (BHP) conditions. With this method, the slope of the square-root-of-time plot applied to any of the producing phases can be used to directly calculate the linear flow parameter xfk without defining pseudovariables. The method requires a set of input pressure/volume/temperature (PVT) data and an estimate of two-phase relative permeability curves. For the field case studied herein, the PVT model is constructed by tuning an equation of state (EOS) from a set of PVT experiments, while the relative permeability curves are estimated from numerical model history-matchingresults. The subject well, an MFHW completed in 15 stages, produces oil, water, and gas at a nearly constant (measured downhole) flowing BHP. This well is completed in a low-permeability,near-critical volatile oil system. For this field case, application of the recently proposed RTA method leads to an estimate of xfk that is in close agreement (within 7%) with the results of a numerical model history match performed in parallel. The RTA method also provides pressure–saturation (P–S) relationships for all three phases that are within 2% of those derived from the numerical model. The derived P–S relationships are central to the use of other RTA methods that require calculation of multiphase pseudovariables. The three-phase RTA technique developed herein is a simple-yet-rigorous and accurate alternative to numerical model history matching for estimating xfk when fluid properties and relative permeability data are available.


2012 ◽  
Vol 15 (03) ◽  
pp. 273-289 ◽  
Author(s):  
Shingo Watanabe ◽  
Akhil Datta-Gupta

Summary The ensemble Kalman filter (EnKF) has gained increased popularity for history matching and continuous reservoir-model updating. It is a sequential Monte Carlo approach that works with an ensemble of reservoir models. Specifically, the method uses cross covariance between measurements and model parameters estimated from the ensemble. For practical field applications, the ensemble size needs to be kept small for computational efficiency. However, this leads to poor approximations of the cross covariance and can cause loss of geologic realism from unrealistic model updates outside the region of the data influence and/or loss of variance leading to ensemble collapse. A common approach to remedy the situation is to limit the influence of the data through covariance localization. In this paper, we show that for three-phase-flow conditions, the region of covariance localization strongly depends on the underlying flow dynamics as well as on the particular data type that is being assimilated, for example, water cut or gas/oil ratio (GOR). This makes the traditional distance-based localizations suboptimal and, often, ineffective. Instead, we propose the use of water- and gas-phase streamlines as a means for covariance localization for water-cut- and GOR-data assimilation. The phase streamlines can be computed on the basis of individual-phase velocities which are readily available after flow simulation. Unlike the total velocity streamlines, phase streamlines can be discontinuous. We show that the discontinuities in water-phase and gas-phase streamlines naturally define the region of influence for water-cut and GOR data and provide a flow-relevant covariance localization during EnKF updating. We first demonstrate the validity of the proposed localization approach using a waterflood example in a quarter-five-spot pattern. Specifically, we compare the phase streamline trajectories with cross-covariance maps computed using an ensemble size of 2,000 for both water-cut and GOR data. The results show a close correspondence between the time evolution of phase streamlines and the cross-covariance maps of water-cut and GOR data. A benchmark uncertainty quantification (the PUNQ-S3) (Carter 2007) model application shows that our proposed localization outperforms the distance-based localization method. The updated models show improved forecasts while preserving geologic realism.


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.


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