Assimilating Microseismic and Well-Test Data by Use of EnKF for Accurate Reservoir Characterization

SPE Journal ◽  
2014 ◽  
Vol 20 (01) ◽  
pp. 186-201 ◽  
Author(s):  
Mei Han ◽  
Gaoming Li ◽  
Jingyi Chen
Keyword(s):  
Test Data ◽  
Reservoir Characterization ◽  
Weighted Average ◽  
Well Test ◽  
Sampling Errors ◽  
Reservoir Properties ◽  
Reservoir Prediction ◽  
Pressure Transient ◽  
Microseismic Data ◽  
Transient Data

Summary The pressure-transient well-test data can be used to determine the thickness-weighted average permeability in a multilayer reservoir. Injection- or production-profile logs (layer rates), if available, may be used to further quantify the layer properties. This paper explores the possibility of the use of microseismic data in place of injection-/production-profile logs for layered-reservoir characterization. The microseismic first-arrival times from the perforation-timing shots of the test well to monitor wells can only resolve the average velocity along its wavepath but are more sensitive to the layer (or region) with high wave velocity (low productivity). On the contrary, the pressure-transient data are more sensitive to the properties of the high-productivity (high-permeability) layers. Therefore, these two types of data are complementary in reservoir characterization. In this paper, we assimilate these two types of data by use of the state-of-the-art ensemble-Kalman-filter (EnKF) method. Layered-homogeneous- and layered-heterogeneous-reservoir examples verified the complementary nature of these two types of data. The porosities and permeabilities in the layered reservoir obtained after assimilating both types of data are comparable with assimilating pressure-transient and layer-rate data. EnKF is a stochastic process, and the final results may depend on the initial ensemble because of sampling errors, sample size, and nonlinearity of the problem. In this paper, we generated 10 different ensembles for each example for better uncertainty quantification. The paper shows that assimilating pressure-transient data only will yield biased estimates of layered-reservoir properties, whereas assimilating both pressure and microseismic data improves the reservoir-property estimation and reservoir-prediction capabilities.

Practical Considerations for Pressure-Rate Deconvolution of Well Test Data

SPE Journal ◽  
2006 ◽  
Vol 11 (01) ◽  
pp. 35-47 ◽  
Author(s):  
Michael M. Levitan ◽  
Gary E. Crawford ◽  
Andrew Hardwick
Keyword(s):  
Test Data ◽  
Transient Behavior ◽  
Test Sequence ◽  
System Response ◽  
Rate Variation ◽  
Well Test ◽  
Pressure Transient ◽  
Pressure Behavior ◽  
Deconvolution Algorithm ◽  
Constant Rate

Summary Pressure-rate deconvolution provides equivalent representation of variable-rate well-test data in the form of characteristic constant rate drawdown system response. Deconvolution allows one to develop additional insights into pressure transient behavior and extract more information from well-test data than is possible by using conventional analysis methods. In some cases, it is possible to interpret the same test data in terms of larger radius of investigation. There are a number of specific issues of which one has to be aware when using pressure-rate deconvolution. In this paper, we identify and discuss these issues and provide practical considerations and recommendations on how to produce correct deconvolution results. We also demonstrate reliable use of deconvolution on a number of real test examples. Introduction Evaluation and assessment of pressure transient behavior in well-test data normally begins with examination of test data on different analysis plots [e.g., a Bourdet (1983, 1989) derivative plot, a superposition (semilog) plot, or a Cartesian plot]. Each of these plots provides a different view of the pressure transient behavior hidden in the test data by well-rate variation during a test. Integration of these several views into one consistent picture allows one to recognize, understand, and explain the main features of the test transient pressure behavior. Recently, a new method of analyzing test data in the form of constant rate drawdown system response has emerged with development of robust pressure-rate deconvolution algorithm. (von Schroeter et al. 2001, 2004; Levitan 2005). Deconvolved drawdown system response is another way of presenting well-test data. Pressure--rate deconvolution removes the effects of rate variation from the pressure data measured during a well-test sequence and reveals underlying characteristic system behavior that is controlled by reservoir and well properties and is not masked by the specific rate history during the test. In contrast to a Bourdet derivative plot or to a superposition plot, which display the pressure behavior for a specific flow period of a test sequence, deconvolved drawdown response is a representation of transient pressure behavior for a group of flow periods included in deconvolution. As a result, deconvolved system response is defined on a longer time interval and reveals the features of transient behavior that otherwise would not be observed with conventional analysis approach. The deconvolution discussed in this paper is based on the algorithm first described by von Schroeter, Hollaender, and Gringarten (2001, 2004). An independent evaluation of the von Schroeter et al. algorithm by Levitan (2005) confirmed that with some enhancements and safeguards it can be used successfully for analysis of real well-test data. There are several enhancements that distinguish our form of the deconvolution algorithm. The original von Schroeter algorithm reconstructs only the logarithm of log-derivative of the pressure response to constant rate production. Initial reservoir pressure is supposed to be determined in the deconvolution process along with the deconvolved drawdown system response. However, inclusion of the initial pressure in the list of deconvolution parameters often causes the algorithm to fail. For this reason, the authors do not recommend determination of initial pressure in the deconvolution process (von Schroeter et al. 2004). It becomes an input parameter and has to be evaluated through other means. Our form of deconvolution algorithm reconstructs the pressure response to constant rate production along with its log-derivative. Depending on the test sequence, in some cases we can recover the initial reservoir pressure.

A New Method to Identify and Quantify Hydraulic Communication between Isolated Reservoir Systems Using Pressure-Transient Data

2021 ◽  
Author(s):  
Hasan A. Nooruddin ◽  
N. M. Anisur Rahman
Keyword(s):  
Test Duration ◽  
Well Test ◽  
Pressure Transient ◽  
Type Curves ◽  
Unlimited Number ◽  
Communication Mechanism ◽  
Wide Range ◽  
Transient Data ◽  
New Formulation ◽  
The Impact

Abstract A new analytical workflow that uses pressure-transient data to characterize connectivity between two originally non-communicating reservoir zones is presented. With this technique, hydraulic communication is clearly identified and corresponding fluid crossflow rates accurately quantified. It is applicable to a wide range of communication mechanisms, including inactive commingled-completion wells, conductive fractures and faults, in addition to behind-casing completion problems. The impact of interference is also captured by handling an unlimited number of wells and communicating media. The solution uses pressure-transient data effectively to diagnose communication and estimate the amount of transported fluids. The new formulation is a general formulation for handling an unlimited number of producing wells and communicating media, which helps analyze pressure responses under the influence of interference. The reservoir system under consideration is assumed to be two-dimensional with two initially-isolated reservoir zones, intersected by an arbitrary number of wells, part of which are active producers while others can be penetrating wells with commingled completion, in addition to other communicating media. The well test duration is assumed long enough for the pressure-transient data to be affected by fluid communication. To demonstrate the applicability of the new model, a synthetic case study is presented to diagnose a fluid-communication mechanism. The system under consideration consists of two isolated reservoirs and two wells: a single producer completed in the top reservoir in which pressure responses are measured, and an offset well connecting both reservoirs through a fluid communication mechanism. Using the model, type-curves have been utilized to diagnose the hydraulic communication in the offset well. The connectivity of the communication channel in the offset well is also estimated by matching the pressure-transient responses of the model with the measured data. The rate of crossflow between the two reservoirs is also quantified as a function of time. It is observed from the log-log plot that higher connectivity values of the cement sheath causes a steeper merging ramp in the transition region, following a period dominated by the producing reservoir. Although the rate of crossflow depends on the magnitude of the connectivity, it is observed that there is an upper limit controlled by the rock and fluid properties of the individual reservoirs. In addition, the pressure regime at the location of the offset well plays an important role in the rate of crossflow. This study presents a novel analytical approach to detect communication from pressure-transient data, and to quantify the magnitude of crossflow rates between reservoir zones. The formulation captures the influence of interference between wells caused by production. While complementing diagnostic information from other sources to confirm fluid movement from isolated zones, the method also quantifies the connectivity of the communicating media, and the amount of crossflow rates as a continuous function of time.

Application of Fractional Calculus in Reservoir Characterization From Pressure Transient Data in Fractal Reservoir With Phase Redistribution

2009 ◽  
Author(s):  
Asha S. Mishra
Keyword(s):  
Fractional Calculus ◽  
Reservoir Characterization ◽  
Dynamic Pressure ◽  
Fracture Network ◽  
Vital Role ◽  
Second Derivative ◽  
Reservoir Properties ◽  
Pressure Derivative ◽  
Pressure Transient ◽  
Vertical Permeability

The present paper describes the use of pressure derivative and second derivative of integral of pressure in a fractal reservoir with matrix participation with phase redistribution in a geological environment that are not possible by conventional techniques. The analysis of this type of data in reservoir characterization is known as “inverse problem” and one can obtain information about interwell and vertical permeability distribution in a reservoir. The fractal geometry in a dynamic pressure transient tests data plays a very vital role for heterogeneity characterization. The pressure transient response is analyzed for flow in a connected fracture network and fracture with matrix participation. The computer aided matching technique for both pressure and its derivative by nonlinear regression techniques are used in estimating the reservoir properties from measured drawdown/buildup and falloff pressure data of heterogeneous reservoir. In the present paper the fractional calculus approach has been utilized to solve the diffusivity equation with phase redistribution in fractal reservoir. The pressure solution of the diffusivity is in terms of Laplace space and its analytical inversion is not possible. We have obtained numerically inversion of the problem and the pressure, pressure derivative, integral of pressure and its first and second derivative has been calculated. The permeability estimated from pressure transient test data of a well are in good agreement with the identified the geological model.

Reservoir Characterization Constrained to Well Test Data: A Field Example

1996 ◽  
Author(s):  
J.L. Landa ◽  
M.M. Kamal ◽  
C.D. Jenkins ◽  
R.N. Horne
Keyword(s):  
Test Data ◽  
Reservoir Characterization ◽  
Well Test

scholarly journals Wellbore Effects in the Analysis of Two-Phase Geothermal Well Tests

1982 ◽  
Vol 22 (03) ◽  
pp. 309-320 ◽  
Author(s):  
Constance W. Miller ◽  
Sally M. Benson ◽  
Michael J. O'Sullivan ◽  
Karsten Pruess
Keyword(s):  
Water Reservoir ◽  
Hot Water ◽  
Well Test ◽  
Two Phase ◽  
Pressure Transient ◽  
Analysis Techniques ◽  
Wellbore Storage ◽  
Storage Effects ◽  
Transient Data ◽  
Well Tests

Abstract A method of designing and analyzing pressure transient well tests of two-phase (steam/water) reservoirs is given. Wellbore storage is taken into account, and the duration of it is estimated. It is shown that the wellbore flow can dominate the downhole pressure signal completely such that large changes in the downhole pressure that might be expected because of changes in kinematic mobility are not seen. Changes in the flowing enthalpy from the reservoir can interact with the wellbore flow so that a temporary plateau in the downhole transient curve is measured. Application of graphical and nongraphical methods to determine reservoir parameters from drawdown tests is demonstrated. Introduction Pressure transient data analysis is the most common method of obtaining estimates of the in-situ reservoir properties and the wellbore condition. Conventional graphical analysis techniques require that. for a constant flowrate well test in an infinite aquifer, a plot of the downhole pressure vs. log time yields a straight line after wellbore storage effects are over. The slope of that line is inversely proportional to the transmissivity (kh/u) of the reservoir. The extrapolated intercept of this line with the pressure axis at a specified time (1 hour or 1 second depending on the units used) gives the factor 0 Cth(re2), which is used to calculate the skin value of a well. In this study, the effects of a two-phase steam/water mixture in the reservoir and/or the wellbore on pressure transient data have been investigated. There have been a number of attempts to extend conventional testing and analysis techniques to two-phase geothermal reservoirs including drawdown analysis by Garg and Pritchett, Garg, Grant, and Moench and Atkinson. Pressure buildup analysis has been investigated by Sorey et al. To solve the diffusion equation that governs the pressure change in a two-phase reservoir analytically, it is necessary to make a number of simplifying assumptions. One assumption is that the fluid compressibility in the reservoir is initially uniform and remains uniform throughout the test. With this approach, it can be shown that a straight line on a pressure vs. log time plot will be obtained, the slope being inversely proportional to the total kinematic mobility When conducting a field test it is rarely possible to maintain the uniform saturation distribution in the reservoir required for that type of analysis to be applicable. In addition, the very high compressibility of the two-phase fluid creates wellbore storage of very long duration. Since most of the available instrumentation for hot geothermal wells (greater than 200C) can withstand geothermal environments for only limited periods, long-duration wellbore storage further complicates data analysis. Thus numerical simulation techniques must be used to study well tests to determine the best method of testing two-phase reservoirs. This work investigates and defines more thoroughly the well/reservoir system when the reservoir or wellbore is filled with a two-phase fluid. Four examples are considered:a single-phase hot water reservoir connected to a partially two-phase wellbore,a hot water reservoir that becomes two-phase during the test,a two-phase liquid-dominated reservoir, anda two-phase vapor-dominated reservoir. State-of-the-art analysis techniques are applied to pressure transient data after wellbore storage effects have ended. In the first example, a nongraphical method of analysis is discussed, which is applicable at early times when wellbore storage effects still dominate the pressure response. Note that our analysis has been done for a two-phase homogeneous, nonfractured reservoir. Previous studies of well test methods for two-phase reservoirs have been restricted to this case. SPEJ P. 309^

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