The Impact of Stimulation Treatment Size on Ultimate Recovery From the Hydraulically Fractured Shale Gas Wells
Item Type Conference Paper
Authors Arias, Daniela;Patzek, Tadeusz
Citation Arias Ortiz, D. A., & Patzek, T. W. (2022). The Impact of Stimulation Treatment Size on Ultimate Recovery From the Hydraulically Fractured Shale Gas Wells. Fourth EAGE Workshop on Unconventional Resources. https://
doi.org/10.3997/2214-4609.202289001 Eprint version Post-print
DOI 10.3997/2214-4609.202289001
Publisher European Association of Geoscientists & Engineers
Rights This is an accepted manuscript version of a paper before final publisher editing and formatting. Archived with thanks to European Association of Geoscientists & Engineers.
Download date 2024-01-04 21:37:56
Link to Item http://hdl.handle.net/10754/686636
Impact of Stimulation Treatment Size on Estimated Ultimate Recovery from the Hy- draulically Fractured Shale Gas Wells
Daniela Arias-Ortiz, Tadeusz W. Patzek
The Ali I. Al-Naimi Petroleum Engineering Research Center, King Abdullah University of Science and Technology, Thuwal 23955- 6900, Saudi Arabia
Summary
Shale gas reservoirs are key to extending a high rate of methane production by a few more decades. Massive stimulation jobs have been developed to produce gas at economic rates. However, optimal design of stimulation and horizontal drilling is challenging due to the unique reservoir conditions. The large stimulation jobs may generate the geometrically complex hydraulic fracture networks that limit gas production. Given importance of mudrock reservoirs, accurately estimating ultimate recovery (EUR) is critical. Here, we study the impacts of the massive hydraulic fracturing jobs on the physics-based scaling curve forecasts. We compare reservoir simulations with varying stimulation job sizes. Also, we incorporate idealized hydraulic fracture geometries into a commercial reservoir simulator and compare the resulting generalized scaling curves. Results show that massive stimulation treatments may not always result in an effective mudrock play development. Additionally, our reservoir simulations reveal a numerical justification for the large fracturing jobs and the unexpected gas production. Finally, we confirm the theoretical predictive power of the scaling curve method.
Introduction
Shale gas reservoirs produce about 70% of all natural gas in the United States and have become a key source of clean(er) energy generation (Arias-Ortiz and Patzek, 2022;Patzek et al., 2013, 2014). These important reservoirs require trustworthy predictive models of estimated ultimate recovery (EUR), which is quite uncertain. A reliable gas production forecast results in improved reservoir insights, optimization of well completions, development of new technologies, and improved field development planning.
Numerous analytical and numerical forecast methods have been derived as a cost-effective methodology to forecast fluid production from mudrock plays (Valk´o and Economides, 1996;Wattenbarger et al., 1998;Clark et al., 2011;Nobakht et al., 2012;Duong, 2014).
(Patzek et al., 2013, 2014) proposed a unique physics-based scaling curve method to predict shale gas production based on the one- dimensional pressure diffusion equation, assuming a cuboid reservoir volume and a constant bottom-hole flowing pressure. The model also assumes the typical vertical, symmetrical and parallel hydraulic fractures, and production coming only from the stimulated reservoir volume (SRV). This model is characterized by two physical parameters: the mass of gas in place in the stimulated reservoir volume (MSRV) and the characteristic time of pressure interference between neighboring hydraulic fractures (τ) (Arias-Ortiz and Patzek, 2022;
Patzek et al., 2019). Later, (Eftekhari et al., 2018) integrated the effect of gas inflow from the external unstimulated volume adjacent to SRV. By now, several studies have shown that this method models the first-order physics behind gas production from the hydraulically stimulated horizontal wells in mudrock plays (Arias-Ortiz and Patzek, 2022;Patzek et al., 2013, 2019;Saputra and Albinali, 2018).
Also, the physics-based scaling curve method is an effective tool to forecasting shale gas production because it is economic with input data, accurate and simple.
Mudrock play development consist of a combination of horizontal drilling, multi-stage completions, and hydraulic fracturing. The stimulation jobs generate highly conductive induced fractures that enable and accelerate hydrocarbon production (Arias Ortiz et al., 2021). Ample evidence suggests generation of complex hydraulic fracture geometries due to the interactions between the hydraulically induced fractures and natural discontinuities (e.g., pre-existing natural fractures and shale bedding planes) (Blanton, 1986;Chuprakov and Prioul, 2015;Tang et al., 2018).
In this work, we built a conceptual model of a typical hydraulically stimulated horizontal well in a shale gas reservoir. Later, we built the numerical reservoir models with different stimulation job sizes. Next, we integrated three idealized but feasible hydraulic fracture geometries (“fracture cases”) into the conceptual model. The simulated gas production is used to predict the scaling curve for each fracture case. Finally, we analyze the behavior of the physics-based scaling curve method by varying stimulation treatment size and hydraulic fracture geometry.
Methods
We built a conceptual reservoir model of a hydraulically stimulated horizontal well in a shale gas reservoir using a commercial reservoir simulator (CMG-GEM). We have described our numerical model in (Arias-Ortiz and Patzek, 2022). We base the reservoir properties on the Marcellus Shale in northeast Pennsylvania. In summary, the conceptual model has a two-phase gas and water flow. We assume generation of a natural fracture network only in the stimulated reservoir volume. In this study, the modeled reservoir pressure gradient is 0.017 MPa/m (∼0.7 psi/ft) to the depth of about 2000 m (∼7000 ft). We use the compaction curve method (pressure-dependent porosity
and permeability multipliers) to account for closure of the hydraulically induced fractures. We also incorporate gas adsorption through Langmuir isotherms.
Figure 1: Hydraulic fracture geometries (left) integrated into the reservoir simulation model built in CMG-GEM (right).
Our work compares three predetermined hydraulic fracture geometry cases to analyze the effect of the stimulated fracture geometry complexity on the physics-based scaling curve method. Our base case has a typical hydraulic fracture geometry, which consists of parallel vertical and planar fractures spaced every 45 m (∼150 ft). Based on Arias Ortiz et al.(2021), this geometry is often found in extensional structural settings. The second hydraulic fracture case increases complexity of the base hydraulic fracture geometry by adding two induced horizontal bedding plane fractures (green planes in Figure 1). The mechanically weak bedding planes may be hydraulically stimulated in mudrock reservoirs characterized by transitional strike-slip to reverse faulting stress regimesArias Ortiz et al.
(2021). The third hydraulic fracture case includes the vertical and planar hydraulic fractures with stimulated vertical natural fractures perpendicular to the hydraulic fracture planes shown in pink in Figure 1 (orthogonal fractures). This fracture scenario may occur in shale reservoirs with a normal faulting stress regime and a low difference between the principal horizontal stress magnitudesArias Ortiz et al.
(2021). We reduced the hydraulic fracture aperture in the second and third fracture cases and kept the same stimulation job size as in the base case. Detailed information about the different scenarios in which these fracture geometries may occur is inArias Ortiz et al.
(2021).
We integrated the described fracture geometries cases into a commercial reservoir simulator (CMG-GEM). We built three additional scenarios of the base fracture case, where we increased the number of hydraulic fractures. We have three scenarios with 35, 50, and 60 hydraulic fractures distributed along a horizontal well. Later, we used the simulated cumulative gas production and the gas mass in place in the stimulated reservoir volume to estimate the generalized scaling curves. Several authors have explained the physics-based scaling curve method in (Arias Ortiz et al., 2021;Patzek et al., 2013, 2014, 2019;Saputra et al., 2021, 2022;Eftekhari et al., 2018, 2020). Our work focuses on stimulation jobs. We study the effects of varying hydraulic fracture geometry complexity and stimulation treatment size on the scaling curve behavior.
Results and Discussion
Large stimulation jobs have become a common practice in the development of shale reservoirs and achieve gas production at high, economic rates. For instance, in 2020, the expected mean value (P50) was about 3000 kg/m (∼2000 lb/ft) and 25 m3/m (∼48 bbl/ft) for proppant and fracturing water intensity in the Marcellus shale (Saputra et al., 2022). Also, the expected mean value (P50) of lateral length was about 2500 m (∼8000 ft). Moreover, average pore pressure gradients exceed 0.13 MPa/m (∼0.7 psi/ft) in some shale plays.
Previous studies discuss the reduction of the principal effective stress magnitudes due to high pore pressure and high organic content (Arias Ortiz et al., 2021, 2022). These mudrock environments, combined with the massive treatments, foster the generation of hydraulic fracture networks geometrically complex that may affect hydrocarbon forecasting.
Figure 2: Right: Generalized scaling curve varying the number of hydraulic fractures in the base fracture scenario, which only has vertical and planar fractures. Left: the generalized scaling curve method for varying complexity of hydraulic fracture geometry (“fracture cases”).
This study demonstrates the effects of idealized but complex fracture geometries on recovery factor. Figure 2 presents the results in terms of the recovery factor, which is the total gas production scaled byMSRV andτArias-Ortiz and Patzek(2022). We calculate the recovery factor accounting only for the mass of gas in place within the stimulated reservoir volume. The right plot compares the scaling curves when the number of hydraulic fractures and wellbore length increase. The plot shows an insignificant increase in the recovery factor when increasing the number of hydraulic fracturing stages. This result confirms that large stimulation treatments may not increase well production. The dashed line is the Marcellus generalized scaling curve presented inSaputra et al.(2022). The left plot compares the scaling curves when varying the hydraulic fracture complexity. The figure shows a decrease in gas production when complexity of the induced fracture geometry increases. The recovery factor of the base fracture case is about 85% based on the stimulated reservoir volume. The stimulation of large horizontal bedding planes (fracture case 2) reduces the estimated recovery factor by some 12%, while the stimulation of perpendicular pre-existing natural fractures (fracture case 3) reduces the estimated recovery factor by about 5%. Gas production is reduced because the interactions between the vertical hydraulic fractures and reservoir discontinuities reduce the energy available to propagate the fractures and increase their conductivity.
Conclusions
Massive stimulations are not always optimal for developing shale gas reservoirs. Moreover, it is crucial to understand the hydraulic frac- ture initiation and propagation based on the reservoir conditions and present-day in situ stress state. Understanding the final hydraulically induced fracture geometry results in realistic gas production forecasting. We conclude that complex hydraulic fracture geometries reduce expected gas production due to a loss of energy on fracture propagation and conductivity. The physics-based scaling curve method has an excellent predictive power confirmed by numerical reservoir simulations; it models all essential physics behind shale gas production.
Acknowledgements
This study was supported by baseline research funds to Professor Tadeusz Patzek from the King Abdullah University of Science and Technology (KAUST). The authors thank the reviewers for their valuable and timely suggestions.
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