QI can be helpful for drilling decisions in unconventional plays
QI is applied routinely to conventional reservoirs, with steps such as rock physics modeling (RPM), fluid substitution and inversion into elastic properties being firmly established in the geophysical workflow and deployed in the successful development of hydrocarbon reserves.1
For unconventional reservoirs, QI is equally valuable, but it has not seen the same take-up, perhaps due to lower development and drilling costs associated with land-based assets. However, QI for unconventional reservoirs is a cause that we are championing here.
Arguably, the principal parameter in characterizing an unconventional reservoir is the total organic carbon (TOC) concentration,2 with its quantification a key to screening reserves for potential development. A QI workflow for low-permeability shale reservoirs can be exploited to maximize returns and reduce the risk of poor drilling decisions. For such a workflow, we require a good log-based TOC determination, followed by rock physics modeling, kerogen substitution and synthetic gather computation, from which characteristics of changes in the signal can be understood. Recasting log points into AI vs. SI space then aids interpretation of a final inversion.
Many steps in a QI workflow are important, regardless of whether we are looking at conventional or unconventional reservoirs; for example, log and seismic data conditioning and velocity verticalization. However, for shales, we need to either replace or enhance certain steps in the workflow. In this section, we’ll focus on some of the new steps, such as TOC determination and kerogen substitution, as well as steps we’ve enhanced, such as rock physics modeling of velocity and density outputs, and synthetic gather calculations using anisotropic reflectivity formulations.
We start our QI workflow for unconventional reservoirs with a robust determination of TOC concentration, achieved by combining the log-based Passey method with a more recently derived theoretical version that relates TOC to bulk density, using prior knowledge of the kerogen porosity.3,4 Here, we average the two TOC calculations, with greater weight given to the bulk density method when only poor-quality core data is available, or toward the Passey method, where there is doubt about the validity of the kerogen porosity.
With a robust TOC log curve derived, the second step of our workflow uses the Alfred and Vernik model for representing an unconventional shale reservoir.4 This considers shale as a mixture of a hydrocarbon pore-filled organic phase—characterized using the kerogen volume, which we derive from TOC values—and a water-filled inorganic phase. To this, we then apply the Vernik-Kachanov method to compute bulk density and bedding normal velocities.2 This uses the separate contributions that porosity and crack-like inclusions make to the kerogen and non-kerogen phases of the shale model, enabling density and velocity RPMs to be built to aid setting parameter values for the two phases, Fig. 1.
The Vp versus TOC RPM is constructed, using the Vernik-Kachanov method, first computing the non-kerogen phase by mixing dry clay, quartz and carbonate proportions to give a constant Vp (horizontal line along top of right cross-plot) and then adding water-filled pores to the non-kerogen phase before it is mixed with kerogen, when porosity is assumed oil-wet. Cracks—parameterized by an effective, stress-dependent crack density factor—are added to the mixed phases, resulting in the lower curve that passes through the cloud of points.
The derivation of these RPMs allows forward modeling of Vp, Vs and density log curves, from which we compute synthetic gathers to show the impact that individual parameters have on the amplitude versus offset (AVO) signal of the seismic data. Because the shale exhibits a strong polar anisotropy, we use a Ruger-based formulation to compute a reflectivity series, which is convolved with a wavelet extracted from the near stack seismic data, to compute gathers.5 The anisotropy is estimated from a derived empirical relationship for shale reservoirs that relates Vp to the epsilon parameter.2,6 Cross-plotting of Thomsen parameters, measured from cores, indicates that a 1:1 relationship between epsilon and gamma, and a ratio of 2:1 between epsilon and delta, in unconventional shale reservoirs is a suitable approximation to derive the full set of Thomsen parameters from epsilon.2
To test the sensitivity of the AVO response of the gathers to different TOC concentrations, we use kerogen substitution to perturb the velocity and density log curves, and compute new sets of synthetics. The procedure is shown using a series of log curves in Fig. 2. First, the TOC log values within our target formation are used as inputs to the RPMs that were set up above, and to forward model a set of theoretical density, Vp and Vs outputs (black curves in tracks 2 to 4). The TOC log is then perturbed, here by 50%, and the RPMs used again to forward model a new set of outputs (purple curves in tracks 2 to 4).
Taking Vp as an example, the calculation is made from two forward-modeled Vp values at each sample, VpTOC (log TOC) and VpTOC50 (perturbed TOC), and computing the difference, VpDifference:
VpDifference = VpTOC50 – VpTOC. (1)
This output is applied to the original Vp log curve, to give use a new curve, VpSubstituted:
VpSubstituted = VpLog + VpDifference. (2)
A similar approach is applied to compute VsSubstituted and DensitySubstituted log curves.
A replacement suite of Thomsen parameter log curves also is required from the substituted Vp log which, when calculated as described above, indicate a reduced anisotropy (track 8). We expect this, since the substituted log curves contain less kerogen, which is a key anisotropy agent.2
The substitution of kerogen by the stiffer non-kerogen phase of the shale model has the effect of increasing the shale’s density and velocities. This hardening is depicted in the RPMs in Fig. 3, showing the points within the shale zone moving along their trend curves toward the lower TOC.
Kerogen substitution has been applied to well data from two separate producing formations that have different characteristics, Fig. 4. The first well (left), used for the figures and discussion above, has a lower thermal maturity level and clay content than the second well (right). These observations, in addition to the higher effective stress in Well 1, mean that a different parameterization is needed to compute Well 2’s RPMs. The two formations are encased within vertically heterogeneous overlaying formations with impedances greater than
Both sets of gathers show a Class IV AVO response, which is expected from having a lower impedance target encased within the harder surrounding material (see the Vp, Vs and density tracks in Fig. 4). This is evident in the left gather of each well, giving stronger negative and positive signals at the top and bottom of the shale target zone. Additionally, it is quantified in the reflection coefficient versus angle of incidence plots at the base of each gather. With the kerogen content reduced by the substitution, the target zones’ impedances harden (right gathers of each well), and the AVO signal decreases by some 40% for both wells.
Casting the points into AI vs. SI space indicates that it is possible to discriminate high TOC from low TOC, and suggests that it would be worth investigating whether simultaneous inversion could map productive formations in the shale reservoir, Fig. 5. Lower TOC values will map into higher values in the AI vs. SI space, as shown by the movement of points following kerogen substitution. Having constructed our QI workflow, we now turn our attention toward an application to field data. In Fig. 6, we show sections through inverted acoustic (AI) and shear impedance (SI) volumes, quality checked with well-log derived impedances from two blind wells that were not used in construction of the low-frequency volume. The workflow used in deriving these volumes followed the methodology described above, culminating in pre-stack simultaneous inversion.
Taking the outputs from the QI workflow further, we use our rock physics modeling to derive isotropic and anisotropic fracture gradient profiles in a vertical well penetrating an unconventional shale reservoir, Fig. 7. The final track of the figure illustrates how the fracture gradient profile computed using the anisotropic information (shown in red) better matches the diagnostic fracture injection test (DFIT) carried out in one of the likely landing zones for a horizontal lateral. A similarly computed profile (blue), without the anisotropy, has failed to match the single DFIT value.2
By using rock physics and the construction of a workflow, we have shown that QI is a viable proposition for making drilling decisions in unconventional reservoirs. Starting with the computation of a TOC log, followed by its use in rock physics modeling, the shale is parameterized in terms of hydrocarbon-associated kerogen and non-kerogen components. The combination of kerogen substitution, together with forward modeling and cross-plotting, is valuable for understanding AVO and inversion outputs, similar to fluid substitution in conventional reservoirs. The discrimination within AI vs. SI space of formations with higher TOC concentrations, indicative of higher hydrocarbon associated kerogen, is demonstrated.
- Simm, R., M. Bacon, “Seismic amplitude,” Cambridge, 2014.
- Vernik, L., “Seismic Petrophysics in Quantitative Interpretation,” Second Edition. SEG 2016.
- Passey, Q. R., S. Creaney, J. B. Kulla, F. J. Moretti and J. D. Stroud, “A practical model for organic richness from porosity and resistivity log,” AAPG Bulletin, 1777-1794, 1990.
- Alfred, D., and L. Vernik, “A new petrophysical model for organic shales,” 53rd Annual Logging Symposium, SPWLA conference paper 2012-217, 2012.
- Ruger, A., “Variation of P-wave reflectivity with offset and azimuth in anisotropic media, Geophysics, 63, 935-947, 1998.
- Thomsen, L., “Weak elastic anisotropy,” Geophysics, 51, 1954-1966, 1986.
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