Abstract
Accurate estimation of the reservoir parameters is crucial to predict the future reservoir behavior. Well testing is a dynamic method used to estimate the petro-physical reservoir parameters through imposing a rate disturbance at the wellhead and recording the pressure data in the wellbore. However, an accurate estimation of the reservoir parameters from well-test data is vulnerable to the noise at the recorded data, the non-uniqueness of the obtained match, and the accuracy of the optimization algorithm. Different stochastic optimization methods have been applied to this address problem in the literature. In this study, we apply the recently developed iterative ensemble Kalman filter in the context of well-test analysis to infer reservoir parameters from the noisy recorded data. Since the introduction of the ensemble Kalman filter (EnKF) by Evensen in 1994 as a novel method for data assimilation, it has had enormous impact in many application domains because of its robustness and ease of implementation, and numerical evidence of its accuracy. While the objective of the standard EnKF approaches is to approximate the statistical properties of geological parameters conditioned to observation, via an ensemble, the objective of the iterative ensemble Kalman methods is to approximate the solution of inverse problems using a deterministic derivative-free iterative scheme. We conducted three case studies of the application of the iterative ensemble Kalman methods for a well-test analysis of a homogenous reservoir model, a dual-porosity heterogeneous system, and a faulted discontinuous reservoir. We demonstrated that the convergence occurs very rapidly almost at the first iterations contrary to the well-known particle swarm optimization algorithm. The maximum relative error for the simulated cases is below 15%, which belongs to the skin factor. Low relative error, narrowed uncertainty range over time, and excellent graphical match obtained between the simulated derivative data and the generated curve by using the iterative EnKF verify the robustness of the developed algorithm even in dealing with complex heterogeneous models.
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Abbreviations
- u ( k+1) :
-
State parameters of the system at the k + 1th iteration within the Kalman filter
- u k :
-
State parameters of the system at the kth iteration within the Kalman filter
- A k :
-
A matrix relating state parameters at the kth iteration to the k + 1th iteration within the Kalman filter
- B :
-
A matrix relating the inputs at the kth iteration to the k + 1th iteration
- x k :
-
System’s input data
- h k :
-
System noise at the kth iteration
- v k :
-
Measurements noise at the kth iteration
- y k :
-
Observational data at the kth iteration
- H k :
-
A matrix relating the state parameters at the kth iteration to the observations at the kth iteration
- F(ψ):
-
Probability density of the model states
- f i :
-
Component number i of the model operator f
- gQg T :
-
Covariance matrix for the model errors
- Q :
-
Variance of the systems’ noise
- R :
-
Variance of the measurement’s noise
- P(h):
-
Probability function governing the system’s noise
- P(v):
-
Probability function describing the measurements’ noise
- T :
-
Time, hr
- J :
-
Number of ensembles within the EnKF algorithm
- y ( j) :
-
Measurements for the jth ensemble in the EnKF algorithm
- ξ ( j) :
-
Measurements’ noise for the jth ensemble within the EnKF
- Γ :
-
Variance of the measurements’ noise
- \(w_{n}^{(j,f)}\) :
-
Predicted state parameters in the EnKF method for the jth ensemble within the EnKF method
- G :
-
Nonlinear model operator
- \(u_{n}^{(j)}\) :
-
State parameters at the nth iteration for the jth ensemble in the EnKF method
- \(\bar{w}_{n}^{f}\) :
-
Ensemble average of the predicted state parameters in the nth iteration within the EnKF method
- \(\bar{u}_{n}\) :
-
Ensemble average of the state parameters in the nth iteration within the EnKF method
- \(C_{n}^{uw}\) :
-
The cross-covariance matrix of the state parameters and the estimated state parameters at the iteration n
- \(C_{n}^{ww}\) :
-
The autocorrelation matrix of the estimated state parameters at the iteration n
- \(u_{(n + 1)}^{(j)}\) :
-
Estimated state parameters belonging to the jth ensemble at the n + 1th iteration after applying the analysis step in the EnKF method
- \(w_{(n + 1)}^{(j)}\) :
-
Predicted state parameters belonging to the jth ensemble at the n + 1th iteration within the EnKF method
- \(\bar{u}_{{\left( {n + 1} \right)}}\) :
-
Ensemble average of the state parameters at the n + 1th iteration
- t D :
-
Dimensionless time
- p wd :
-
Well dimensionless pressure
- s :
-
Laplace variable
- \(\bar{p}_{\text{wd}} \left( s \right)\) :
-
Well dimensionless pressure solution in the Laplace domain
- Φ:
-
Porosity fraction
- r w :
-
Wellbore radius, ft
- h :
-
Reservoir thickness, ft
- c t :
-
Total compressibility, psi−1
- q :
-
Well flow rate, STBD
- p i :
-
Initial pressure, psi
- µ :
-
Viscosity, cp
- B o :
-
Oil Formation Volume Factor, Rbbl/STB
- K :
-
Permeability, md
- S :
-
Skin factor, dimensionless
- Λ :
-
Interporosity flow coefficient, dimensionless
- ω :
-
Fracture storativity ratio, dimensionless
- L f :
-
Perpendicular fault distance from well, ft
- f(s):
-
Laplace function for the dual-porosity model
- k 0 :
-
Modified Bessel function of second kind and zero degree
- k 1 :
-
Modified Bessel function of second kind and first degree
- C D :
-
Dimensionless wellbore storage coefficient, dimensionless
- d D :
-
Fault dimensionless distance, dimensionless
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Appendix: Well pressure behavior at the Laplace domain
Appendix: Well pressure behavior at the Laplace domain
Solution for the well pressure at the Laplace medium is provided at this section for different reservoir systems studied at this paper.
1.1 Infinite acting homogenous reservoir [69]
where S and CD are, respectively, skin factor and the dimensionless wellbore storage coefficient; s is the Laplace parameter; k0 and k1 are, respectively, modified Bessel functions of second type with orders zero and one.
1.2 Infinite acting dual-porosity reservoir with PSS interporosity flow [70, 71]
where f(s) is defined by the following equation:
ω is the fracture storativity ratio and λ stands for the interporosity flow coefficient representing how strong is the communication between the matrix and the fracture system. Other parameters are the same as for Eq. 15.
1.3 Infinite acting homogenous reservoir with a linear fault [72]
where dD is the fault dimensionless distance defined by \(d_{\text{D}} = \frac{{L_{f} }}{{r_{w} }}\).
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Bazargan, H., Adibifard, M. A stochastic well-test analysis on transient pressure data using iterative ensemble Kalman filter. Neural Comput & Applic 31, 3227–3243 (2019). https://doi.org/10.1007/s00521-017-3264-5
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DOI: https://doi.org/10.1007/s00521-017-3264-5