spe132967用实时数据与定期监测数据进行油气藏管理

Reservoir Management With Real-Time and Periodic Surveillance DataC.S. Kabir, SPE, D. Ismadi, SPE, and S. Fountain, SPE, Hess CorporationAbstractThis paper demonstrates the value of collecting and interpreting real-time data. With an intensive data gathering strategy,starting at wells inception to the mature production phase, we show how transient pressure and rate data can be used tomanage a complex carbonate gas reservoir. In particular, reservoir connectivity is discerned with pulse testing and with the leading-edge p/q graph, and continuous updates of in-place volume are made with both static and dynamic material-balance methods and corroborating the same with rate-transient analysis. Interwell connectivity information was deduced during underbalanced drilling by way of interference test between two pairs of wells. Thereafter, transient-pressure tests on individual wells characterized the layered, dual-porosity system, with production logs corroborating the notion of layering. Production maturity over three years has paved the way for estimating connected in-place gas volume associated with each well using the transient-PI, and also with a new method introduced here.This new approach entails plotting both static and dynamic material-balance data on the same graph, yielding the same solution. Errors associated with real-time rate measurements presented interpretation challenges for rate-transient analysis; however, application of a physics-based filtering algorithm resolved this issue. Flow-after-flow tests that were embedded in monthly variable-rate production allocations, in turn, allowed us to obtain average-reservoir pressure explicitly to do the static material-balance analysis.IntroductionMost wells completed in difficult environments are typically instrumented with pressure, temperature, and flow sensors to collect real-time data. While instrumentation in a deepwater well is seldom questioned, this issue lacks clarity in other settings. Perhaps one common thread to any field operation is demonstration of economic benefits that can be realized with real-time reservoir management. In this context, Kragas et al. (2004) made a compelling business case for a particular asset by showing avoidance of pressure buildup surveys and wireline-surveillance costs, and minimizing lost production. However, the intrinsic value of such sensing can be made in an objective fashion, as advocated by Gilbert et al. (2009). Notwithstanding the difficulties of tying surveillance benefits to economics, technical benefits of extracting information, such as changing permeability and skin with time using permanent downhole sensors have been noted by many authors. Some of these studies include those of Olsen and Nordtvedt (2006), Haddad et al. (2004), and Coludrovich et al. (2004). Weiland et al. (2008) presented a case study showing applications of pressure-transient test analysis and production logging to monitor evolving field performance in a deepwater asset. Horne (2007) summarized many of the technical benefits of surveillance. Studies providing operational guidance from surveillance and implementation of the notion of digital oilfield are many; those of Shyeh et al. (2008), Maskeri et al. (2008) are worthy of note. In this study, we show that the use of leading-edge analytical tools can help assess ongoing well performance and reserves estimation to meet the needs of internal assessments and that of regulatory bodies. In terms of analytical tools, four independent methods corroborated the in-place volume calculations. These methods include transient-PI (Meidiros et al. 2007), dynamic material-balance (Agarwal et al. 1999), static-material balance, and rate-transient analysis (Palacio and Blasingame 1993). We also show a graphical method for combining the static and dynamic material-balance methods. Because individual in-place volumes connected to each well are additive, the well connectivity question was answered through pulse testing during underbalanced drilling (UBD) and the use of p/q method (Kabir and Izgec 2009) during the production mode. The reason for nonlinear behavior on the p/q graph was also explored in this study.Learning Reservoir Connectivity and Drive MechanismsReservoir Connectivity. The issue of reservoir connectivity is foremost in the minds of all pro-fessionals managing reservoirs. Yet, the extent of connectivity is often not learned until production maturity occurs. Principally, we have indirect and direct methods for establishing reservoir con-nectivity amongst wells. Although fluid analyses, such as PVT properties and geochemical finger-printing serve the first-order purpose, no quantitative measure emerges when the degree of well-towell connectivity is sought. Fluids and the fault/seal analysis fall into the category of indirect methods. Amongst direct methods, tracer and transient testing offer the opportunity to quantify reservoir connectivity. However, tracer tests are only suitable in secondary or tertiary recovery operations. Transient-pressure tests involving pressure pulses, generated by carefully designed rate perturbations, provide quantitative information about interwell connectivity. Among the plethora of publications in this regard, the studies of Kamal (1979), Dinges and Ogbe (1988), and Ogbe and Brigham (1989) are worthy of note. Execution of pulse tests may be time-consuming because of large interwell distances, coupled with lowrock permeability and high-system compressibility in a given setting. However, UBD gives an opportunity to collect the necessary information. In this dry-gas field, pulse test data were gathered in two pairs of wells during UBD operation. For brevity we discuss one of those two tests. Fig. 1 presents the match for well A on the log-log graph, while the well C was being drilled underbalanced some 2.8 km away. Although only a very small segment of a typical interference-test response developed despite 850 hours shut-in, the interwell connectivity question was answered, nonetheless. Fig. 2 presents the pressure match on the Cartesian scale with the attendant flow rate history in the pulsing well undergoing underbalanced drilling.Fig. 1 Modeling interference test response, Well A. Fig. 2 Modeling interference test response for well A based on well Cs production history during UBD.When coupled with real-time data gathering capability, all of the leading-edge reservoir management tools can be brought to bear to learn about reservoir connectivity. For example, the use of p/q graph (Kabir-Izgec 2009) can shed light on reservoir connectivity when pseudosteady-state (PSS) flow develops in all the wells. Those wells that are being depleted from the same connected-pore volume will overlap on the p/q graph. As Fig. 3 demonstrates all wells exhibit the same trend at variousbackpressures. Note, however, that Well D shows a flat response owing to very low-withdrawal rate coupled with high backpressures. In fact, at the highest backpressure of 2,700 psia, the points are displaced upward from the trend. Appendix A explores the nonlinear behavior of the p/q graph to shed further light.Fig. 3 The p/q graph suggests well connectivity at various back pressures.Drive Mechanisms. During course of this study, questions often surfaced whether any aquifer support was being felt in this reservoir. To address this question, we computed drive indices assuming small pot aquifer and formation compressibility. As Fig. 4 shows, neither the aquifer support nor the formation compressibility plays any role of significance. Put another way, expansion drive is by far the dominant mechanism. We, therefore, conclude that no degree of aquifer support has occurred over three years of production in this gas reservoir.Fig. 4 Marginal contribution of formation compressibility as a drive index.Understanding Reservoir Heterogeneity With Pressure-Transient AnalysisA buildup test over 300 hours was run on well A, preceded by a flow period of about equal duration in October 2003. This long-duration test revealed many features of the system. Initially, we used a two-layer, dual-porosity model (Warren and Root 1963) to obtain the match. However, presence of two dips in the derivative signature prompted us to pursue a match with the triple-porosity model, espoused by Abdassah and Ershaghi (1986). Fig. 5 displays the match quality. Fig. 5 Pressure-derivative match with a two-layer, triple-porosity model.Note that to mimic the late-time derivative response exhibiting the unit-slope line, the upper layer (Layer-1) was assigned a lower kh value than the lower layer (Layer-2). At the same time, Layer-1 is a relatively short-radius (2,866 ft), bounded system in contrast to Layer-2, where much larger reservoir radius of about 31,000 ft was needed for history matching. The nature of crossflow from Layer-1 into Layer-2 is depicted in Fig. 6, thereby explaining the unit-slope line on the late-time derivative signature.Fig. 6 Layer crossflow during well shut-in explains the late-time unit-slope derivative behavior.Another way of corroborating dual-porosity response is to examine the low-frequency production data. In this context, the Agarwal plot (Agarwal et al. 1999) provides the necessary clues. This notion was recently confirmed by Whitaker et al. (2008) while studying a sparsely fractured reservoir in the Middle East. Earlier, Da Prat et al. (1981) showed that a dualporosity system should yield a flat response on a pressure-normalized-rate/time graph. The flat signature is a manifestation of continual feeding of the fracture or vugular network by the matrix system. Amid scatter, Fig. 7 suggests the notion of flat response. We point out that the x-axis on Fig. 7 is a simplified version of that required for quantitative material-balance calculations. Because our objective is to use this tool for diagnosis only, we can use the simplified version as shown.Fig. 7 The modified-Agarwal graph suggests dual-porosity response.A buildup test lasting about 138 hours was conducted on the well D, preceded by a flow-after-flow test to obtain deliverability during 22-30 October 2008. We used a dual-porosity system with two intersecting sealing-faults to reproduce the late-time response, as exhibited by the pressure-derivative signature in Fig. 8. That a dual-porosity system is operative can be discerned from the flat response on the Agarwal plot, as shown in Fig. 9.Fig. 8 Dual-porosity response with an intersecting fault apparent in Well D.Fig. 9 The modified-Agarwal graph suggests dual-porosity response, Well D.Estimating In-Place Volume With Static- and Dynamic-Material Balance MethodsIn this section, we explore how we obtained in-place gas volume connected to each well. We used transient-PI, and staticand dynamic-material balance methods to confirm the solutions obtained. In the next section, we corroborated these in-place volumes with the traditional rate-transient analysis. Again for brevity, we exemplify the results with two wells. We found application of the transient-PI method (Medeiros et al. 2007) useful on two counts. First, the graph gives diagnostic clues about the nature of porosity system, single or dual. Second, it yields a good estimate of the connected porevolume by a trial-and-error solution. Fig. 10 showing the flat response suggests dual-porosity response, which is consistent with earlier observations. Fig. 10 Transient-PI graph suggests dual-porosity system and yields connected PV, Well C.Estimating reliable average-reservoir pressure (pav) from shut-in tests is daunting, especially in tight reservoirs with lateral barriers and/or layers, where shut-in times can be exceedingly long. The two buildup tests discussed earlier underscore this point. However, one way of skirting shut-in tests for gathering pav lay in conducting flow-after-flow tests and also by generating IPR curves with the variable-rate production data, as shown in the preceding paper (Ismadi et al. 2010). Here, we show that the pav values so derived lend themselves to the traditional static-material-balance (p/z vs. Q plot) treatment. Equally important is that the in-place volume so estimated can be verified on the same plot by using the dynamic material-balance method, espoused by Agarwal et al. (1999) and Mattar and McNeil (1998). Fig. 11 presents such a plot for Well C. We think that simultaneous presentation of both methods on the same graph is very illuminating because they should yield the same solution. To our knowledge, this simple graphical presentation has not been made before. Appendix B explains the underlying equations, leading to the generation of this plot.Fig. 11 Both static- and dynamic-material-balance methods yield the same OGIP, Well C.Interestingly enough, the OGIP value derived from the combined methods in Fig. 11 is corroborated by that of the transient-PI technique, as shown in Fig. 10. We note that given the scatter in the late-time data in the transient-PI method, obtaining the exact plateau by a trial-and-error approach is somewhat subjective. In this respect, the combined graphical approach shown in Fig. 11 is more reliable. Both the flowing and shut-in pressures track the model-generated values quite well, as depicted in Fig. 12.Fig. 12 Models match the shut-in and flowing BHPs, Well C.Corroborating In-Place Volumes With Rate-Transient AnalysisProduction data of Well A turned out to be most problematic in that severe pressure/rate data incoherence became apparent. For instance, Fig. 13 showing the overall mismatch of pressure, rate, and cumulative production clearly underscores the p/q data harmony issue.Fig. 13 Complete mismatch results owing to lack of pressure/rate harmony.To understand the p/q data coherence issue, we graphed wellhead temperature (WHT) with the reported rates. Fig. 14 showing the original data in pink suggests that the spurious WHTs, sometimes even exceeding BHT of 193 oF, speak to the main problem at hand.Fig. 14 WHT anchors rate data filtration.Difficulties surfaced when we sought to estimate rate with either the choke correlation or the fluid-flow and heat-transfer modeling in the wellbore. The main issue is that both WHTs and WHPs measurements are suspect in that they do not correlate well with rate. The uncertainties associated with the rate measurement itself put us in a circular argument.Therefore, to circumvent these issues we attempted simple noise filtering. As Fig. 14 shows, the green points essentially contained the noise, thereby affording us an opportunity to do the rate-transient analysis. Note that the increasing trend of WHT is honored with increasing rate, thereby obeying physics of flow and the attendant heat transfer. Appendix C presents the theoretical argument for direct correlation of WHT with flow rate.With the noise-filtered data, we arrived at a much more palatable solution. As Fig. 15 demonstrates that the material balance has been preserved, thereby instilling confidence in the in-place volume calculations. In this context, our experience shows that both the static- and dynamic-material balance methods yield solutions that are close to that obtained from RTA, such as those reported here; that is, within 5.5%. In contrast, there is always some degree of uncertainty with the transient-PIapproach in that it involves a trial-and-error solution requiring subjective judgment about attainment of the horizontal line amid data scatter.Fig. 15 Coherent p/q data leads to gas-in-place estimation with RTA.In contrast to the previous well, Well D provided self-consistent data for rate-transient analysis; Fig. 16 showing the loglog response or the inverted-Blasingame plot makes this point. Low cumulative production justifies the flat response or absence of boundary-dominated PSS flow. Fig. 17 presents the overall history match. The match quality shows selfconsistency of pressure with rate, thereby lending confidence in analysis. Because of lack of well-defined PSS flow period, the in-place volume estimation has very large uncertainty and is, therefore, not reported in any form.Fig. 16 The log-log response showing flat derivative suggests absence of PSS flow.Fig. 17 Consistent signatures of all three curves suggests good solution.DiscussionTechnical benefits of real-time reservoir surveillance have been espoused by many authors. In fact, asset-specific and corporate-wide adoption of surveillance practice, chronicled by authors from major operators and vendors speak to its increasing acceptance in the industry. In this study, we endeavored to make the best use of dynamic surveillance data for managing an asset with a few wells in a difficult terrain and geologically complex environment. Logistical challenges for collecting logs, core, and seismic data, coupled with geological complexity have slowed down our effort to build an earthmodel. We recognize that an earth model is essential to developing a numerical flow-simulation model, leading to probing infill opportunities and also that in the adjacent area, to keep up with long-term contractual obligations.Given this c