A New Method of Characterizing the Stimulated Reservoir Volume Using Tiltmeter-Based Surface Microdeformation Measurements

Abstract

Unlocking shale gas has been extremely successful during the last decade. Nevertheless, new challenges will continuously arise. One of the most pressing current issues is to know the stimulated reservoir volume (SRV), the part of the reservoir that actually received fracturing fluid. The most widely used technology for estimating SRV is downhole microseismic mapping. Under many conditions, it yields reasonably accurate/reliable information about SRV (Mayerhofer et al. 2008). Unfortunately, it requires an observation well in close proximity to the treatment well in which to place the geophone string used to sense the small seismic signals. This requirement often makes it impossible to provide an SRV estimate for many of the hydraulic-fracture treatments that could benefit from this information. This paper presents an alternative to the downhole-microseismic SRV mapping. A new method, stimulated reservoir characterization (SRC), is based on surface microdeformation measurements obtained with a precision surface tiltmeter array. Tiltmeter-based hydraulic-fracture diagnostics have been successfully used for more than two decades on more than 10,000 hydraulic-fracture treatments. SRV is typically calculated for highly jointed shale reservoirs or coal seams in which injected fluids can inflate a myriad of interconnected hydraulic fractures in two or more dominant orientations. The measured surface deformation is the superposition of all the deformation fields resulting from the inflation of each individual fracture. This makes the deformation-based approach for SRV estimation a convoluted process and requires much more complex tilt-data analysis than does a simple planar hydraulic fracture. This paper describes a new technique for tilt-based SRV estimation, which is capable of resolving spatial distribution, orientations, and volume percentages of the major components of a fracture network. Hence, the new technique allows not only an insight into the areal penetration of treatment fluids into the reservoir, but also an understanding of how multiple joint sets, each with unique orientations, are actually accepting the injected fluids and proppant. This paper includes synthetic data examples and SRV results derived by applying the new technique to a hydraulic-fracture-stimulation project in the lower Eagle Ford shale formation.

Application of Fractal Geometry in Evaluation of Effective Stimulated Reservoir Volume in Shale Gas Reservoirs

“…(16)- (18). Finally, we analyzed the effect of fracture space on gas migration using parameters for the Eagle Ford well (as shown in Table 2).…”

“…[9][10][11] The SRV can be calculated by microseismic imaging, induced-fracture-monitoring technology and mathematical models based on fracture propagation regularity. [12][13][14][15][16][17][18] However, the SRV calculated by conventional methods differs greatly difference from the effective SRV (ESRV) estimated by flowperformance curves or actual field production. 6,19,20 Compared with SRV, ESRV is a more important prerequisite for confirming the success of hydraulic fracturing and predicting the production of hydraulic-fracturing wells in shale reservoirs.…”

A Validation Assessment of Microseismic Monitoring

“…More importantly, it is important to know if any complex microseismic activity is actual dilation of fractures in multiple azimuths or merely shearing along multiple azimuths because the difference can have a significant effect on the resultant conductivity and production through the azimuths that are oblique to the main fracture plane. Walser and Roadarmel (2012) present a study of horizontal-well fracturing in the shallow Eagle Ford with both microseismic and surface tiltmeters, but the tiltmeters were analyzed using a more sophisticated inversion (Astakhov et al 2012) to deduce where fracture planes were opening and the orientations of those planes. In this way, actual volumetric changes in the reservoir could be determined, allowing inferences about the behavior of oblique planes and the significance of any complex microseismicity.…”

“…20 shows a comparison of the data, with the microseisms shown as diamonds and the surface tiltmeter results shown as grid-block features. The analysis (Astakhov et al 2012) breaks the reservoir layer into grid blocks and inverts the surface tilt response to determine the best fit of the fracturing behavior in all of the reservoir blocks. In this example, the magenta-colored grid blocks show horizontal fractures, the cyan grid blocks show vertical fractures (with the azimuth indicated by the darker lines), and the mixed color blocks have both horizontal and vertical components.…”

“…The limitation on the use of tiltmeter data is that its application generally requires some inverse modeling to extract parameters, which itself has some level of uncertainty. Surface tiltmeters (Wright et al 1998a) can be used to determine fracture azimuth and components (complexity), and in many cases they can be used to estimate the areal extent of fracturing (Astakhov et al 2012). Downhole tiltmeters (Branagan et al 1996a;Wright et al 1998b) are useful for evaluating fracture height in the far-field and can also be used to estimate fracture length when more than one monitor well is available.…”