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Summary The deconvolution analysis technique that evolved with development of the deconvolution algorithms by von Schroeter et al. (2004), Levitan (2005), and Levitan et al. (2006) became a useful addition to the suite of techniques used in well-test analysis. This deconvolution algorithm, however, is limited to the pressure and rate data that originate from a single active well on the structure. It is ideally suited for analysis of the data from exploration and appraisal well tests. The previously mentioned deconvolution algorithm can not be used with the data that are acquired during startup and early field development that normally involve several producing wells. The paper describes a generalization of deconvolution to multiwell pressure and rate data. Several approaches and ideas for multiwell deconvolution are investigated and evaluated. The paper presents the results of this investigation and demonstrates performance of the deconvolution algorithm on synthetic multiwell test data. Introduction Pressure-rate deconvolution is a way of reconstructing the characteristic pressure transient behavior of a reservoir-well system hidden in the test data by well-rate variation during a test. The deconvolution analysis technique that evolved with development of the deconvolution algorithms by von Schroeter et al. (2004), Levitan (2005), and Levitan et al. (2006) became a useful addition to the suite of techniques used in well-test analysis. It has been implemented in commercial well-test analysis software and is routinely used for analysis of well tests. This deconvolution algorithm, however, is applicable only for the case when there is just one active well in the reservoir. It is ideally suited for analysis of exploration and appraisal well tests. The previously described deconvolution algorithm cannot be used for well-test analysis when there are several active wells operating in the field and the bottomhole pressure measured in one well during a well test is affected by the production from other wells operating in the same reservoir. The deconvolution algorithm has to be generalized so that it is possible to remove not only the effects of rate variation of the well itself but also the pressure interferences with other wells in the reservoir. As a result, we would be able to reconstruct the true characteristic well-pressure responses to unit-rate production of each producing well in the reservoir. These responses reflect the reservoir and well properties and could be used for recovering these properties by the techniques of pressure-transient analysis. Multiwell deconvolution thus becomes in a way a general technique for interference well-test analysis. The problem, however, is that the interference pressure signals produced by other wells are small compared to the pressure signal caused by the production of the well itself. These pressure interference signals are delayed in time and the time delay depends on the distance between respective wells. All this makes multiwell deconvolution an extremely difficult problem.
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.
- Europe (0.68)
- North America > United States (0.46)
Summary The challenges of reducing or eliminating emissions associated with well-testing operations during reservoir appraisal have revived interest in water injection/falloff tests. However, the two-phase flow and injection-induced temperature changes associated with injection/falloff tests complicate the problem of well-test analysis. Strictly speaking, these effects make the pressure transient problem nonlinear and preclude using conventional superposition technique to construct solutions for variable rate problems. In this paper, we present a new analytical method for accurate solution of the pressure transient problem for two-phase flow associated with water injection/falloff tests. The solution algorithm allows one to compute the solution for any stepwise constant rate sequence that includes multiple injection and falloff periods. The method is general, and can be used for water injection governed by any physically meaningful relative permeabilities, as well as for pistonlike displacement. Introduction Water injection/falloff tests are normally associated with waterflood projects. Recently, interest in this type of well test has developed in the area of reservoir appraisal. In the vast majority of situations associated with exploration activities, there is no infrastructure and equipment in place to collect and export the hydrocarbons produced during well tests. The common practice used in the industry is to burn the produced fluids. The demands to reduce emissions during well tests created enormous pressure to avoid these tests altogether. This brings large uncertainties to the reservoir appraisal, and increases the investment risk if a decision is made to sanction a project and to develop the field. In most cases, drilling additional wells to reduce appraisal risks is not an option in view of the enormous costs of wells in frontier and deepwater explorations areas. Replacing a production/buildup test sequence by an injection/ falloff sequence solves the problem of emissions. However, this significantly complicates the problem of well-test analysis. First, the character of the system changes. Instead of single-phase flow, we are now faced with two-phase water/oil flow governed by relative permeabilities. Second, injection of cold water induces temperature changes in the formation and brings additional complications to pressure behavior through temperature effects on the oil and water viscosities. Third, injection of water may result in the formation fracturing and in coupling of rock mechanics and fluid flow problems. It is therefore critically important for successful test interpretation to avoid fracturing and to inject water at below the formation fracturing pressure. Pressure transient behavior during water injection and falloff tests has been studied by a number of authors. Most of the efforts were concentrated on homogeneous reservoirs with radial flow geometry. Many original insights into this process came from numerical experiments. This opened the way for development of analytical models that are based on simplified flow equations that capture the leading order effects controlling transient pressure behavior. In one of the first studies on this subject, by Verigin, the two-phase flow during water injection was represented as a two-bank system in which injected water displaces formation fluid in a pistonlike manner. This model was later extended to pressure falloff tests. The two-bank model may be a reasonable approximation of the flow conditions for the case of very favorable mobility ratio when the mobility of injected water is much less than the mobility of reservoir fluid. Note that the two-bank model does not account for saturation variation within the water-invaded region. Numerical experiments, however, indicate that the saturation gradients that develop during water injection do have significant effects on transient pressure behavior. It has also been realized early on that the Buckley-Leverett model of oil-water displacement process provides an accurate approximation of the water saturation distribution within the water-invaded region. Abbaszadeh and Kamal developed a generalization of the two-bank model, the so-called multibank model, which is intended to account for water saturation variation within the water-invaded region. In a multibank model, the invaded region is divided into several banks with constant water saturation within each of the banks. The water saturation profile is thus approximated as a stepwise constant function. Bratvold and Horne were able to remove this restriction (stepwise constant saturation distribution) and developed an analytical solution for a constant rate injection problem that allows for continuous saturation variation within the water-invaded region.
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (1.00)
- Energy > Oil & Gas > Upstream (1.00)