For a hydrostatic reservoir, the initial reservoir pressure P0 is determined by the hydrostatic pressure gradient [ΔPHydrostatic/dL] times reservoir depth (d):
The hydrostatic pressure gradient typically is [ΔPHydrostatic=0,45psi/ft]. The pressure change due to production or injection is obtained from:
μ is the viscosity (psi·day or Pa·s, or Poise), k is the permeability (ft2 or m2 or Darcy).
The reservoir pressure consequent to the well flow will be:
Each pressure field plot is generated by evaluating the pressure differential Equation (31) for a large number of complex-valued coordinates z, in conjunction with Matlab’s command ‘contourf’.
The streamline tracing method uses a first order Eulerian displacement scheme. The initial particle position is z0 (in complex coordinates) at time t0 (unit in (s); see Weijermars et al.  for a non-dimensional approach). The velocity field V(z,t) needs to be solved to obtain the velocity vector components (vx and vy) for all positions of tracer elements at times t1, denoted by z1(t1):
The term v(z0(t0)) in expression (33) is the velocity of the traced particle at time t0 and location z0, for which the velocity field V(z,t) is used. The size of the time step is Δt. The locations of tracer elements at time tj are:
In Matlab we evaluate expression (34) by using the corresponding velocity field V(z,t) as follows:
In order to maintain smooth streamlines and time-of-flight contours (TOFCs), the time step Δt needs to be smaller when the strength of point sources/sinks increases. The velocity field plots in this study map the spatial variation in the magnitude of the fluid phase velocity, which is obtained by taking the absolute value |V(z,t)| at a given time t. Each velocity magnitude contour plot uses a large number of complex-valued coordinates z, in conjunction with Matlab’s command ‘contourf’.
Our flow visualization method can show both the matrix drainage by the main fractures and any deeper reach into the matrix when micro-cracks occur as offshoots from the main fractures. First we show the drainage area when no micro-cracks develop. The locations of the main fractures for each stage of the multi-staged fractures in our prototype well were inferred from the micro-seismic image recorded by the operator during the frack job (Figure 9a). The images of Figure 9b–d show the reservoir response to drainage by a hydraulically fractured, horizontal well after 1 month of production. A summary of input data is given in Table 1. The table includes a description of hydraulic fracture geometry and dimensions, the well productivity based on Duong parameters, original oil in place (OOIP) parameters, and other reservoir parameters.
Figure 9. (a) Map view of imaged seismic events centered about a horizontal well during fracking of each stage (individual color bands). Adjacent seismic imaging well is highlighted (olive green) in left of image. (b) Velocity field (m/s) in after 1 month of well-life; field dimensions in m. (c) Pressure change (MPa) relative to the original reservoir pressure. (d) Detailed pressure gradient map of central region of the fracked well.
Table 1. Key parameters used in this study.
The velocity plot (Figure 9b) reveals that the oil migrates fastest near the tips of the fractures, with an interior region between the fractures draining much slower. Classical analytical flow models  asserted that no fluid flow would occur from matrix into the fracture tips. Only flow of matrix normal to the fracture is assumed, which is clearly not what we see in our CAM simulations where flow near the fracture is fastest and not following sub-parallel or linear streamlines, but more complex particle paths. The simplification of linear flow would lead to overestimation of diffusion to the fracture, particularly when such fractures are short.
Velocity Field Versus Pressure Plots
The velocity field near hydraulic fractures is rarely visualized in commercial reservoir simulators [93,94]. Pressure depletion plots (Figure 9c) are commonly used as a proxy for drainage of the reservoir. The reservoir pressure when production starts is replaced by an evolving pressure gradient. The steepest pressure gradients occur at the onset of production, with local variations of higher and lower gradients. Figure 9c has the steepest pressure gradients between the red and blue zones (with tighter contour spacing), which broadly coincide with the region of higher flow velocities peripheral to the frac tips (Figure 9b).
In commercial simulators, pressure plots resemble the analytical solution of Figure 9c but have limited resolution due to finite grid size and computational time limitations. Our model based on closed-form formulae provides infinite resolution and allows for the computation of a detailed pressure contour map for the central region between the fracs (Figure 9d).
Due to relatively shallow pressure gradients, flow in the central zone is slowest. Contoured for smaller changes, pressure gradients still exist in the central region (Figure 9d), but pressure differences are more subtle than elsewhere in the fracked region. The shallow pressure gradient is the reason why the flow is nearly stagnant in the central region between the frac clusters (Figure 9b).
Implications for Production Efficiency
A prototype well that had 50 month historic production data was history matched with a decline curve using a Duong model [81,91,95]. The well type curve for Wolfcamp shale in the Midland Basin, West Texas, is given in Figure 10a. The production forecast was used for production allocation to individual fractures proportionally to each frac stage (see Section 3.6). The width of the drained matrix region around the fractures is visualized using the allocated well flux (Figure 10b).
Even after 40 years of production, large portions of the matrix between the fractures remain un-drained. The drainage area and pressure plots were generated using a flow reversal principle: the produced fluid was injected back into the reservoir at the same rate as produced, which allows the construction of time-of-flight contours for the drained rock volume (Figure 10b). All pressure plots show positive pressure, but are equivalent to pressure depletion plots by taking the negative of the pressure scale.
Figure 10. (a) History matching monthly production (bbls/month, red curve) for 50 months with Duong decline curve (blue curve). (b) Drained area next to the main hydraulic fractures after 480 months (40 years). (c) Cumulative production (in bbls; left axis) and recovery factor (in %; right axis) over time (in months, horizontal axis).
Figure 10b shows the total drainage area for 40 years of production and the narrow region drained corresponds to the cumulative production given by the type curve (Figure 10a). OOIP of the prototype well was estimated for a well spacing of 320 acre/well resulting in a recovery factor of only 6% after 40 years, with 4% recovery already achieved after 5 years.
Figure 10c plots the cumulative production and the corresponding recovery factor appreciation over a 40 year field life. These results are based on the history matched and drained rock volume (Figure 10b) obtained by our simulation method. Large undrained (dead) zones occur between the principal fracture zones. However, when micro-cracks are present to drain the deeper matrix, the dead zones may be narrower, which was further investigated.
The CAM model can be adapted to account for drainage by micro-cracks, were these to occur, that reach deeper into the matrix. Such fractures are currently below the resolution of fracture diagnostics. Unknown matrix micro-cracks acting as hydraulic conduits may drain the matrix further away from the main fractures. Assuming micro-cracks may develop in hydraulically fractured shale reservoirs, the degree of micro-crack contribution to shale drainage will vary with the density and hydraulic conductivity of such cracks.
The flow support that hydraulic fractures receive from the microcracks was simulated using line dipoles (see Section 3.5). The strength of dipoles was systematically varied relative to the hydraulic fractures (modeled by interval sources) to show the effect on the drained region in our model. Examples of micro-crack assisted flow in the central region of the multistage fractured well of Figure 9 are shown in Figure 11.
What the dipole fracs drain around the micro-crack tips as a consequence of their conductivity being higher than that of the matrix is transported toward the main hydraulic fractures. The depth of the drained area and length of the dipoles representing the micro-cracks is determined by the assigned strength of the dipole relative to that of the interval source used to model the hydraulic fracs. The combined dipole strength of micro-cracks that are connected to a particular hydraulic fracture is variable: equal to the hydraulic fracture strength (Figure 11a), five times the hydraulic fracture strength (Figure 11b), and 10 times the hydraulic fracture strength (Figure 11c).
Figure 11. (a–c) Row 1: Pressure contour maps (in MPa) after 1 month drainage for the same central region of Figure 9d, now including flow through micro-cracks normal to the main fractures. Dipole strength of (a) is scaled to match hydraulic conductivity of the main fractures. Cases (b,c) are respectively 5 and 10 times stronger. (a–c) Row 2: Velocity field (log scale in m·s−1) for cases a-c in Row 1. Local velocities are higher near the micro-crack tips when conductivity is high. Overall area drained stays constant.
The pressure plots (Figure 11a–c, row 1) show pressure contours begin to bungle near the micro-cracks when the hydraulic conductivity is larger. Such relatively steep pressure gradients are absent in Figure 9d, which is free of micro-cracks. The flow velocities are clearly enhanced around the micro-cracks (Figure 11a–c, row 2), due to the steep pressure gradients induced near the cracks (Figure 11a–c, row 1).
As a result, the micro-cracks would extend the drainage region deeper into the matrix (Figure 11) as compared to situations where micro-cracks are absent (Figure 10b). The velocity peaks are confined to the immediate vicinity of the micro-cracks (Figure 11b,c, row 2), which concur with the locations were pressure gradient are steepest and pressure contours bungle (Figure 11b,c, row 1).
The consequent drained region for each case is visualized in Figure 12. The higher resolution images show locally enhanced pressure gradients (Figure 11, row 1) and flow velocity peaks (Figure 11, row 2) occur near the micro-cracks, which facilitate the flow from the deeper matrix regions toward the main fractures.
Figure 12. (a–d) Drained region after 1 month of production (red contours). (a) micro-cracks absent, (b,c) micro-cracks with increasing hydraulic conductivity as in Figure 11a–c.
Our study suggests that matrix drainage by hydraulic fractures can be accelerated by micro-crack systems. Micro-crack tips drain deeper into the matrix as a consequence of their conductivity being higher than that of the matrix and thus may act as hydraulic fairways. The results show that micro-cracks will improve the drainage depth, but the existence of such micro-cracks is beyond the resolution of current fracture diagnostics.
Although our method can visualize flow around the main fractures and any micro-cracks at high resolution, uncertainty remains about the precise location and geometric complexity of the micro-cracks. The relative strengths of dipoles, in our model, may be systematically varied relative to the interval source to show the effect of the micro-crack drainage with different hydraulic conductivity. Darcy’s law diffusion based flow descriptions was used to characterize the drainage of the SRV.
This assumption implies nano-scale effects such as osmosis and capillary effects do not invalidate the macroscopic flow description. Future work should account for such nano-scale effects. In any case, micro-cracks would enhance and deepen the matrix area drained by the main hydraulic fractures.
Micro-cracks incorporated in the simulation method are synthetic and hypothetical. Such micro-cracks may contribute to the productivity of the well. However, any net gain in productivity is not accounted for in our model, because the net flux is history matched against the actual production, which fixes the 40 year production curve.
What the model shows is that the drained rock volume, in the absence of micro-cracks, remains confined to the immediate vicinity of the hydraulic main fractures. Consequently, we are not underestimating well productivity or recovery factors. The main issue is that the region that will be drained may shift to the interior of the matrix (away from the main fractures) due the presence of micro-cracks.
Several simplifying assumptions are acknowledged. Multiphase flow is not included in our current model, for Wolfcamp B wells, reservoir pressures do not stay above the bubble-point for the lifetime of a well. However, we think the effect on well productivity in the oil window is immaterial due to the low gas content of the wells and late life well productivity contributes little to the cumulative production of the well. Although multiphase flow is not included in our current model, bubble-point effects can certainly be incorporated (subject of new work).
Our conclusion that the highest gradients will occur around the fracture tips, may be nuanced when proppant density varies and premature screen out occurs at fracture tips during the fracture treatment. When the flux around the tip is limited by the connectivity from the tip of the fracture to the wellbore, the flux will shift inward, which is accounted for in a forthcoming paper from our research group .
We conclude that even when micro-cracks develop minor splays into the matrix, the region between the larger master fractures will remain largely un-drained. The flow velocity and pressure gradients for both the main hydraulic fractures and micro-cracks will be largest near the fracture tips. The smallest velocities and flow stagnation occur in the zones between competing fractures, where the pressure gradients are shallower and become zero in pressure culmination points.
The consequent oil entrapment between fractures would not occur in conventional, high-permeability reservoirs, because even slowly moving reservoir fluids would still reach the well on the time scale of the field life (5–50 years). However, unconventional reservoir rocks such as shale with permeabilities in the nano-Darcy range have ultra-low flow rates, even near the fracture tips, in the order of 10−7 m·s−1, the rate at which finger nails grow.
The cluster spacing in the Wolfcamp well studied here is already tight (60 ft). Closer spacing of the fractures would accelerate early production but still creates flow stagnation points between the fractures. However, the un-drained oil occurring between the closely spaced fractures (in low permeability reservoirs with ultra-low flow rates) may be recovered by the refracking of shale wells with perforations placed midway between the prior cluster spacing and fracture initiation points. The second generation of hydraulic fractures will tap into the earlier, stagnant flow regions. The refracs will improve the recovery factors and this may boost well economics accordingly.
Conceptualization, R.W.; Methodology, R.W.; Software, A.v.H. and R.W.; Validation, R.W. and A.v.H.; Formal Analysis, R.W. and A.v.H.; Investigation, R.W. and A.v.H.; Resources, R.W.; Data Curation, R.W.; Writing-Original Draft Preparation, R.W.; Writing-Review & Editing, R.W.; Visualization, R.W. and A.v.H.; Supervision, R.W.; Project Administration, R.W.; Funding Acquisition, R.W.
Funds for this study were provided by the Texas A&M Engineering Station (TEES) and the Crisman-Berg Hughes Consortium. Proprietary well data were made available by Pioneer with support of both the Texas Oil and Gas Institute and University Lands.
Conflicts of Interest
The authors declare no conflict of interest.
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