SPE-176931-MS Sweet Spot Identification and Prediction of Frac Stage Performance Using Geology, Geophysics, and Geomechanics - Application to the Longmaxi Formation, China Yang, X., Wang, X., SCGC, Aoues, A., SIGMA 3, Ouenes, A., FracGeo Copyright 2015, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Asia Pacific Unconventional Resources Conference and Exhibition held in Brisbane, Australia, 9 11 November 2015. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract Microseismic data combined with surface seismic and treatment data was used to understand the performance of frac stages at three wells drilled in the Longmaxi formation, China. In well H1, the microseismic could not provide information on the frac stages since it was affected by fault reactivations. In the wells H2 and H3, the use of maximum curvature derived from surface seismic showed qualitatively strong correlations with microseismicity. To quantify the effects of shale properties on the frac stage performances, seismic attributes were used to derived geologic models of porosity, total gas, fracture density and Poisson s Ratio which were combined to form the Shale Capacity. The examination of the Shale Capacity with the production log of H1 could explain the performance of the frac stages away from the faults. The same observations could be made using the good shale capacity away from the faults to explain the important difference in production between H2 and H3. Given the importance of the faults and their geomechanical impact on the performance of the frac stages, a geomechanical workflow able to simulate the interaction between the hydraulic and natural fractures is applied to the H1 well. The resulting strain and J Integral are able to explain the performance of the frac stages at H1 thus confirming the importance of the natural fractures and the need to account for their geomechanical effects. Introduction According to the U.S. Energy Information Administration s 2013 estimate, China has an estimated 31.6 trillion cubic meters (tcm) of technically recoverable shale gas resources, almost as much as the United States and Canada combined. During 2014, China drilled 200 wells bringing the total number of wells to 400 thus becoming the second largest shale-gas producer. China has targeted the Longmaxi formation in the Sichuan Basin, located in south-central China, as its initial shale gas exploration and development objective. The early effort in shale gas development has been led by Sinopec and China National Petroleum Corporation's (CNPC) PetroChina. According to China's Ministry of Land and Resources (Today in Energy, 2015), the two national companies are on schedule to reach 600 MMcf/d of shale gas production by the end of 2015. CNPC has drilled 125 shale wells, bringing 74 of them into production, and is on schedule to produce 250 MMcf/d of shale gas by the end of this year. Sinopec is
2 SPE-176931-MS currently producing 130 MMcf/d from the Fuling shale gas field in the Sichuan Basin. Like in North America, these first shale wells in China have shown important variability in their performance. From the North American experience and the wells drilled in China it appears that the lateral and vertical heterogeneity of the shale and the natural fracture system have a major impact on the well performance. In other words, well performance does not depend only on drilling and completion technologies but also on the shale geologic properties which could be optimal in certain locations called sweet spots. From the North American experience, it appears that the geologic sweet spots are mostly influenced by TOC, porosity, brittleness and fracture density which when combined form the Shale Capacity (Ouenes 2014). This paper describes an integrated study where geophysical, and geologic, data are integrated to build the Shale Capacity model which will be used along with interpreted microseismic, treatment data and production logs to better understand the geologic sweet spots which could be used to optimize well placement, landing zones, and geosteer the well trajectory in the best rock possible. Once the geologic sweet spots identified, geomechanics is used to better understand the effect of the natural fractures on the resulting stimulation so ultimately the frac stage number and spacing could also be optimized. Study Area A detailed description of the Longmaxi formation can be found in Jin et al. (2015) and in this paper we will focus on the Wei area in South of Sichuan basin. In this area, the Longmaxi formation depth is less than 4000 m and is mainly made of grey-black silty shale, underlain by the Wufeng black limestone. The thickness of the reservoir varies from 30 to 65m with the percentage of brittle minerals in the shale ranging from 30% to 75%. The study area is heavily faulted and the fault throw within the base of Upper Ordovician is less than 25m. These conditions make the study area very attractive for shale gas development. At the time of the study, there was only one vertical well from which three laterals H1, H2 and H3 were drilled to target the Longmaxi formation shale. The maximum horizontal stress direction in the considered area is estimated to be almost in the East-West direction. Figure 1: Map view of H1, H2 and H3
SPE-176931-MS 3 The first lateral, H1, is approximately 1000m long and was drilled toe-down and has an azimuth of 315 degrees. The wells H2 and H3 are approximately 1500m long, have an azimuth of 135 and 132 degrees respectively and are separated by about 400m as shown in Fig. 1. The log data recorded at the three wells (Fig. 2) show that the GR, TOC and the density is higher in H2 and H3 than the H1. The H1 wellbore has production logs which will be used to study the performance of each frac stage. A surface micro-seismic survey was acquired during the stimulation of H1 between July and September in 2013. Another surface micro-seismic was acquired during the stimulations of H2 and H3 from November to December 2014. The results of these microseismic surveys are analyzed and interpreted in the next section. Figure 2: GR, TOC, Total Gas (TG), and density logs at H1, H2 and H3. Notice the low TOC at H1 Microseismic Interpretation The well H1 was designed with 12 completion stages. Due to the presence of a fault, stage four was skipped resulting in 11 stages. Both H2 and H3 have been designed and executed with 17 completion stages. The recorded micro-seismic data was processed and interpreted. Fig. 3 shows the map view of the interpreted micro-seismic data in the three wells. The size represents the event magnitude, and the color represents different stages. The following observations could made for the micro-seismic events at each well. 1) The micro-seismic events at H1 are limited to two major linear events while H2 and H3 show diffuse and complex events in all their frac stages (Fig. 3) 2) The distribution of the micro-seismic events at H1 is different from those seen at H2 and H3, and are nearly oriented North to South. The microseismic events are clustered into 2 areas. Most of the events are clustered near the toe and the middle of the wellbore. The events of stage 1 to 3 are concentrated
4 SPE-176931-MS near the toe. The events recorded from the stimulation of stages 5 to 12 are also concentrated near the toe and 800m away from it. The differences in H1 s event magnitude is relatively large. The larger magnitude events are located North of the toe. 3) The magnitude of the events at H3 are nearly equivalent, and they tend to follow Northeast trends. The concentration of events at H3 is higher than those found at H2. 4) The magnitude of the events at H2 are nearly the same and their concentration is relatively less than the one seen in H3. Figure 3: Map view of H1, H2 and H3 interpreted micro-seismic events. Notice the two major linear events in H1 and the diffuse events in H2 and H3. Since there is a major difference in the microseismic events between the two linear events along H1 as compared to the diffuse events seen in H2 and H3, the data from H1 will be analyzed individually, and the data from H2 will be compared to H3. Generally, the micro-seismic relationship between frequency and magnitude follows the Gutenberg- Richter relationships similar to those used in earthquakes: log10(n) =a-bmw where N: the number of events whose magnitude is larger than Mw, a: constant which represents the total level of the micro-seismic event, b : slope which is the relative ratio between strong and weak magnitude. The frequency-magnitude relationship is usually used to describe the nature of the microseismic( Grob and van der Baan, 2011; Vogelaar et al., 2013). In most cases, the b-value associated with fault re-activation is approximately equal to 1, and the expected successful hydraulic fractures have b-
SPE-176931-MS 5 values larger than 1 and around 2. Fig. 4 shows the b value for the three considered wells. The orange curve is for H1, the yellow curve is for H2 and the green curve is for H3. The horizontal axis in Fig. 4 represents the stage number while the vertical axis represents the b-value. From Fig. 4 we can see that the majority of H1 s b-value are approximately around 1, which is supporting the fault re-activation hypothesis. On the other hand, the majority of H2 s b-value is near 2 except at stage 2 where it is about 0.8. The majority of H3 s b- value are between 1 and 2, which is an indication of interaction between hydraulic and natural fractures. When the b-value is less than 1, the micro-seismic is more concentrated (stage 3 of H3), linear (stage 9 of H1) or forms clusters (stage 2 of H2), with larger magnitude events. When the value of b is larger than 2, the distribution of the events seems to diverge in many directions (Fig. 5). To better understand these microseismic observations, we will compare them to treatment data to find possible correlations. Figure 4: b- value along the frac stages of H1, H2 and H3 in the Y axis versus the frac stage number along the X axis Figure 5: Various b- value varying from 0.6 to 2.3 showing the impact on the distribution of the micro-seismic events Analysing Treatment Data Most of the frac stages had an average pumping rate of 12-14 m 3 /min, with some exception at the toe
6 SPE-176931-MS and the heel as shown in Fig. 6. It interesting to note that the pressure varied along the frac stages of H2 but remain similar in all the stages of H3. The distribution of the microseismic events along H2 stages showed also lot of variations in their distribution thus supporting the presence of many natural fractures near the wellbore that were responsible for the leak-off observed in the pressure behavior. The distribution of the microseismic events along H3 were similar and support the stable pressure which could be related to lack of high intensity natural fracturing near the wellbore. To better understand the differences between H2 and H3 we analyze the Poisson s Ratio and breakdown pressure along the frac stages of H2 and H3. Figure 6: Pumping rate in m3/min for all the frac stages at H2 and H3 The comparison of Poisson s Ratio and breakdown pressure between H2 and H3 (Fig. 7) shows very little variations. When examining the details of H2 Poisson s Ratio and breakdown pressure, we notice that stages 1 to 10 have a higher value than the last seven stages 11 to 17, with the highest value at stage 5, and the lowest value at stage 11. For well H3, the higher values are from stages 1 to 11 while the stages 12 to 17 seem to have lower values. For H3, the highest value is at stage 9 and stage 12 has the lowest value. From Fig. 7, it appears that the high breakdown pressure is found where the Poisson s Ratio is high which is usually an indication of high differential stress resisting the hydraulic fracturing and creating mostly linear microseismic events. To better understand the behavior of these wells, and to estimate the Poisson s Ratio in the entire study area, the 3D seismic will be used along with the well data.
SPE-176931-MS 7 Figure 7: Poisson s Ratio (top) and breakdown pressure (bottom) along the frac stages of H2 and H3 Correlating Microseismic Events to Seismic Curvature The distribution of the stimulated rock depends on the interaction between the hydraulic and natural fractures. The micro-seismic can in certain cases (when not dominated by fault reactivation events) help understand the interaction between the hydraulic and natural fractures and the resulting stimulated rock. Knowing the distribution of the natural fractures could play a major role in optimizing the hydraulic fracturing. To better understand the distribution of the natural fractures 3D seismic could play a major role. For example, Du et al (2015) used seismic attributes to interpret their micro-seismic events. Among the seismic attributes commonly used in these interpretations are the structural attributes such as curvature. Fig. 8a shows the maximum curvature co-rendered with the micro-seismic events recorded during the stimulation of stages 1 to 3 of H1. The maximum curvature shows clearly two linear features oriented in the North- South and close to the wellbore. One feature is about 100m west of the toe and the other is about 600m away from the toe. Most of the events recorded during the stimulation of stages 1 to 3 are concentrated along the fault clearly shown in the maximum curvature. It is important to note that there are no microseismic events extending southwards of the well. Due to the fault reactivation caused by stages 1 to 3, stage 4 was skipped. Figure 8a: Maximum curvature corendered with microseismic events from stages 1 to 3 at H1
8 SPE-176931-MS. Figure 8b shows the maximum curvature co-rendered with the micro-seismic events recorded at stages 5 to 12. The micro-seismic events are still following the NS faults which are getting reactivated. Most of the micro-seismic events are concentrated near the toe. A small number of microseismic events are about 550m away from the toe and seem also to follow another fault. Figure 8b: Maximum curvature corendered with microseismic events from stages 5 to 12 at H1 Unlike H1, the well H2 shows no fault reactivation but is still influenced by the presence of the faults. Fig. 9a shows for example the micro-seismic events from stages 8 to 15 of well H2 which seems to follow the fault imaged in the maximum curvature. Figure 9a: Maximum curvature corendered with microseismic events from stages 8 to 15 at H2 Fig. 9b shows the microseismic events from stages 3 to 7 of H2 which display a diffuse nature that correlates well with the absence of major faults. Figure 9b: Maximum curvature corendered with microseismic events from stages 3 to 7 at H2
SPE-176931-MS 9 Well H3 does not cross any major fault and as a result has many more diffuse events as shown in Fig. 10a displaying the micro-seismic events recorded during the stimulation of stage 6 to 12. All the micro-seismic events appear to be constrained inside the area where there are the least fracturing and are bounded by a strong NW lineament 350m south of the wellbore. Figure 10a: Maximum curvature corendered with microseismic events from stages 6 to 12 at H3 Fig. 10b shows a part of the well H3 that crosses small faults that seem to create natural fractures responsible for the diffuse events seen between the faults as well as some events that follow the fault. From these observations we can see that seismic attributes could help identify the main faults but cannot provide the detailed fracture model that affects each frac stage. To achieve this goal a more detailed analysis of the fractures and other rock properties is considered in the next section Figure 10b: Maximum curvature corendered with microseismic events from stages 13 to 15 at H3 Quantitative Integration Using G&G and Shale Capacity The previous section illustrated the importance of the natural fractures and their impact on the resulting microseismicity which reflects the differences in the stimulated volume. Qian et al. (2015) showed that the importance of natural fractures when studying the casing deformation problems. However, the geologic sweet spots are not only related to the natural fractures. Guo and Li (2015) focused on the importance TOC and brittlness but the North American experience (Reagan et al., 2013, Ouenes et al., 2014, Newgord, et al. 2015) has shown that all the above properties are equally important to identify the geologic sweet spots which could be quantified with the Shale Capacity. It is also observed in North American shales that the Shale Capacity can be correlated to microseismic data and well performances. To derive the shale drivers that make up the Shale Capacity high resolution pre-stack inversion is used to estimate multiple seismic attributes that are used to derive with a neural network the geologic models of porosity, total gas, fracture density and Poisson s Ratio. These four geologic shale drivers (Fig. 11) are normalized and multiplied to form the Shale Capacity.
10 SPE-176931-MS Figure 11: The four key shale drivers used to compute the Shale Capacity and displayed along the H1 wellbore Fig. 12 shows a cross section of shale capacity along the H1 wellbore. The warm colors represents the high values while the cool colors represent the low values. When all of the 4 key shale drivers have high value, the shale capacity is high. If one shale driver has a low value, the Shale Capacity tends to be low. The normalized Shale Capacity shows high values (higher than 65%) at frac stages 1 to 3, and from stage 7 to 12. An initial production log was available at H1 and shows high production from stages 6 and 7 and the highest contribution from stage 10. The examination of the shale capacity and the production log (Fig. 12) with the Poisson s Ratio (Fig. 11) seems to indicate that geomechanical effects (low differential stress in low Poisson s Ratio zones) play a major role in the performance of the frac stages thus the need to include geomechanics which will be discussed in the last section. Figure 12: Shale capacity along the H1 wellbore (bottom) and the production log at each frac stage (top) Without production logs at H2 and H3, we will rely on the knowledge acquired in H1 to better
SPE-176931-MS 11 understand the performance of the frac stages at these two wells where there is a complex fracture network around their wellbores. Qian et al (2015) and the observations made at H1 indicate that the natural fractures can improve the production of H2 and H3. Figure 13a and 13b show the relative shale capacity sections along H2 and H3. The warm colors represent the high values or geologic sweet spot. To better highlight the faults and their associated natural fractures, the lineaments imaged with the maximum curvature are co-rendered with the Shale Capacity cross sections. These cross sections are used to explain the fact that the production from H2 is about twice the one seen in H3. As indicated earlier the frac stage performance is not only affected by the geologic conditions but also by the geomechanical conditions which depend highly on the presence of the faults. If we consider a cut-off of 65 for the Shale Capacity and measure the length of the wellbore crossing this good rock outside the zones where the possible faults are, we find in H2 a length of 26 units and only 12 units in H3 which could explain the production contrast between the two wells. Figure 13a: Relative Shale Capacity along H2 corendeted with maximum curvature Figure 13b: Relative Shale Capacity along H3 corendeted with maximum curvature Quantitative Integration Using 3G
12 SPE-176931-MS The previous sections used geophysics and geologic to identify the geologic sweet spots. Unfortunately, all three wells H1, H2, and H3 exhibited a behavior that cannot be explained entirely with only geology and geophysics and there is a pressing need to use geomechanics to derive a better understanding of the well performance. For example, in well H1 the fault reactivation prevented the use of microseismic to understand the stimulated volume since all the microseismic events were associated to the fault reactivation and no events were recorded as a result of the hydraulic fracturing. In wells H2 and H3, there is a large difference in production and the faults seems to have again played a major role as shown in the previous section. To answer all these questions, the geomechanical modeling of the interaction between the hydraulic and natural fractures is needed and will be applied to well H1. A new geomechanical workflow (Aimene and Ouenes, 2015) will be used on well H1 to address some of the geomechanical questions. The workflow uses the Material Point Method (MPM) combined with the continuous fracture modeling to model the interaction between the natural fractures and the hydraulic fractures. In the considered well H1, the natural fractures are estimated from the maximum curvature which is used to estimate the Equivalent Fracture Model (EFM) as illustrated in Ouenes et al. (2015) and shown in Fig. 14. The resulting natural fracture description is input into an MPM geomechanical simulator where the 11 hydraulic fractures are added as shown in Fig. 14. The simulation of the hydraulic fracturing is performed by putting pressure in the hydraulic fractures. The resulting strain is shown in Fig. 15 and allows a better understanding of the H1 performance. The high production from stage 10 can be easily explained with the resulting strain where we can see that on both sides of the well H1, the strain values are very high thus showing the stimulated reservoir volume which could not be imaged by the microseismic which was affected by the fault reactivation. Figure 14: Equivalent Fracture Model (left) and MPM grid (right) around well H1
SPE-176931-MS 13 Figure 15: Strain derived from the MPM geomechanical simulation around H1. Notice the high strain at stage 10 where the production is the highest. Another major benefit derived from the MPM geomechanical simulation is the ability to compute the J integral which represents the fracing energy and its ability to successfully stimulate the rock. The concept of J integral is explained in detail in Aimene and Ouenes (2015) and its application to solve multiple shale problems is illustrated in Ouenes et al. (2015). In the case of H1, we see that the J integral correlates well with the production log and the highest value of the J integral is achieved at stage 10. Figure 16: J Integral derived from the MPM geomechanical at stages 1, 6 and 10 for well H1 Conclusions
14 SPE-176931-MS The analysis of microseismic data combined with seismic structural attributes and treatment data allow a better understanding of the frac stage performances. To extend the results found at the wells, surface seismic could be used in pre-stack elastic inversion to derive multiple seismic attributes that could be used to derive geologic drivers that can be combined to form the Shale Capacity model. When comparing two wells with different performances, the Shale Capacity could be used to indentify the most likely sections of the wellbore that could be contributing to production. In the case of the studied wells H2 and H3, their differences are in ligne with the lengths of the good Shale Capacity crossed by their wellbores and away from the major faults intersecting them. Since the performance of the wells cannot be explained only with geologic and geophysical information, geomechanics is added to answer the remaining questions. Using a new geomechanical workflow, the performance of the frac stages at the H1 well could be explained with the strain and the J Integral derived using the geomechanical simulation of the interaction between the hydraulic and natural fractures. Acknowledgements The authors thank CNPC and the management of SCGC for the authorization to publish this paper. References Aimene Y., Ouenes, A., (2015) Geomechanical modeling of hydraulic fractures interacting with natural fractures Validation with microseismic and tracer data from the Marcellus and Eagle Ford, Interpretation, Vol. 3, No. 3 (August 2015); p. 1 18, http://dx.doi.org/10.1190/int-2014-0274.1 Du, J., Zhang, J., Sun, X., Wan, X., Xu, G., 2015, Integrated interpretation of microseismic and seismic data in a tight sandstone reservoir, paper URTeC 2153724 presented at the 2015 Unconventional Resources Technology Conference, July 20-22, San Antonio. Guo, T., Li, J., 2015, Integrated geophysical technologies for unconventional reservoirs and case study within Fuling Shale Gas Field, Sichuan Basin, China, paper URTeC 2152914 presented at the 2015 Unconventional Resources Technology Conference, July 20-22, San Antonio. Today in Energy, ( June 26, 2015) Argentina and China lead shale development outside North America in first-half 2015, http://www.eia.gov/todayinenergy/detail.cfm?id=21832 M. Grob & M. van der Baan (2011). Inferring in situ stress changes by statistical analysis of microseismic event characteristics. The Leading Edge, vol. 30, N. 11, p. 1296-1302 Ouenes, A. 2014. Distribution of Well Performances in Shale Reservoirs and Their Predictions Using the Concept of Shale Capacity. Presented at SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, 25-27 February. doi:10.2118/167779-ms Ouenes, A., Bachir, A., Boukhelf, D., and Fackler, M. 2014. Estimation of Stimulated Reservoir Volume Using the Concept of Shale Capacity and its Validation with Microseismic and Well Performance: Application to the Marcellus and Haynesville. Presented at SPE Western North American and Rocky Mountain Joint Meeting, Denver, 17-18 April. doi:10.2118/169564-ms. Ouenes, A., Umholtz, N., Aimene, Y. (2015) Using geomechanical modeling to quantify the impact of natural fractures on well performance and microseismicity: application to the Wolfcamp, Permian basin, paper URTeC 2173459 presented at the 2015 Unconventional Resources Technology Conference, July 20-22, San Antonio. Jin, Z., Li, M., Hu, Z., Gao, B, Nie, H., Zhao, J., 2015, Shorten the learning curve through technological innovation: A case study of the Fuling shale gas discovery in Sichuan basin, SW China, paper URTeC 2152994 presented at the 2015 Unconventional Resources Technology Conference, July 20-22, San Antonio.
SPE-176931-MS 15 Newgord, C., Mediani, M., Ouenes, A., O Conor, P., 2015, Bakken Well Performance Predicted from Shale Capacity, paper URTeC 2166588 presented at the 2015 Unconventional Resources Technology Conference, July 20-22, San Antonio. Qian, B., Yin, C., Li, Y., Xu, B., Qin, G., 2015, Diagnostics of casing deformation in mutli-stage hydraulic fracturing stimulation in lower Silurian marine shale in Southwestern China, paper URTeC 2174637 presented at the 2015 Unconventional Resources Technology Conference, July 20-22, San Antonio. Reagan, J., Wojcik E., Fackler, M., Ouenes, A. 2013. Predicting Well Performances Using the Shale Capacity Concept: Application to the Haynesville. AAPG Search and Discovery: Article # 41204 Vogelaar, A. Oates, S.J., Herber, R. & Winsor, J., 2013. On the relationship between levels of sei smicity and pump parameters in a hydraulic fracturing job. 4th EAGE Passive Seismic Workshop, Amsterdam, March 17-20, 2013, Program with Abstracts, 75-79