# Excess Anisotropy: A Method to Predict Frequency of Resistive Fractures in the Wolfcamp Shale

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## Abstract

**:**

## 1. Introduction

## 2. Materials

## 3. Methods

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## 4. Results and Discussion

## 5. Conclusions

- (1)
- We find VSH to be a strong predictor of anisotropy, and the residual in that estimate relative to cross-dipole-measured anisotropy is a predictor of resistive fracture frequency in these horizontal wells;
- (2)
- Applying this method to determine the presence of fractures using only the computed VSH and cross-dipole sonic logs has the potential to provide a rough estimate of relative fracture frequency when image logs are not available, and allows for a better understanding of seismic-wave propagation in anisotropic formations;
- (3)
- This method can potentially speed up fracture-analysis response time, and has the potential to be used not only as an answer product for wireline customers but as a live answer product for logging while drilling (LWD) customers and for calibration of relative fracture intensity predictions from seismic data when image logs are not available.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Examples of (

**a**) image quality issues due to stick and pull along with cuttings interference causing an image quality where bedding is visible, but fracture identification is difficult, and (

**b**) acoustic quality issues due to borehole rugosity and mud cake.

**Figure 2.**Well logs and observed shear-wave anisotropy. Track 1 shows the image caliper log relative to the bit size and poor acoustic- and image quality flags. VSH in the center track (with units of volume fraction) has color fill of the VSH values with lower VSH in lighter colors (mostly carbonates) and higher VSH values in darker colors (shales). The next track to the right contains the anisotropy curve in blue calculated from measured velocities. The rightmost track contains the processed fast shear (red curve) and slow shear (blue curve) slowness curves used to calculate the anisotropy.

**Figure 3.**Histogram of VSH values (v/v) over the study well (x-axis), frequencies (left y-axis) and cumulative frequencies (right y-axis and black curve) A number of dominant lithologies are apparent, including clean carbonates (VSH less than 0.1), dirty limestones (VSH from 0.15 to 0.25), shales (VSH above 0.25) and clay-rich shales (VSH above 0.7). The shales with low VSH indicate high quartz content in the fine-grained fraction.

**Figure 4.**Histogram of absolute value of percent anisotropy (using Equation (1)) over the study well (x-axis), frequencies (left y-axis) and cumulative frequencies (right y-axis and black curve). Most shales have anisotropy greater than 5%.

**Figure 5.**Portion of the static wellbore resistivity image within the Wolfcamp (bottom track) with overlain fracture interpretation (middle track) and fault dip and strike (third track) using fracture dip symbols to display the direction of strike and dip. The dip angle can be determined by the location of the dip symbol in the track (0–90°).

**Figure 6.**Suggested workflow for fracture frequency prediction starting with quality control of well logs, processing, interpretation and evaluation of data, followed by the linear estimation of anisotropy and calculation of residuals.

**Figure 7.**Cross-plot showing VSH in v/v units (x-axis) and dipole acoustic anisotropy in percent (y-axis). The cross-plot is colored by VSH values, with lower values in the lighter colors (carbonates) and higher values in the darker colors (shales). The sample size of the dataset (nb) is 8697 datapoints. The correlation (R) between the predictor variable VSH and the response variable anisotropy is 0.602.

**Figure 8.**Cross-plot showing VSH in v/v units (x-axis) and dipole acoustic anisotropy in percent (y-axis) in a second well. The cross-plot is colored by VSH values, with lower values in the lighter colors (carbonates) and higher values in the darker colors (shales). The sample size of the dataset (nb) is 4547 datapoints. The correlation (R) between the predictor variable VSH and the response variable anisotropy is 0.660.

**Figure 9.**Plot showing a strong correlation in the first well between the cross-dipole sonic anisotropy (CXD) and the anisotropy predicted using Equation (2), and a strong correlation between the residuals and resistive fracture frequency when smoothed with a 100 ft Gaussian operator. The first track to the right of depth displays the caliper data colored by the mud cake (tan)/washout (aquamarine), along with the image quality flags (red) and acoustic quality flags (blue). The black curve, in the third track, is the volume of shale (VSH) log showing obvious lithological variability. The blue curve, in the fourth track, is the dipole acoustic anisotropy. The pink curve, in the fourth track, is the predicted anisotropy using Equation (3). The fifth track contains the calculated residuals in purple and the resistive fracture frequency in black.

**Figure 10.**Cross-plot in the first well showing residuals (difference between the dipole acoustic anisotropy and the calculated anisotropy) on the (x-axis) and resistive fracture frequency (y-axis), after resampling at 25 feet. The cross-plot is colored by VSH (shale volume fraction) values, with lower values in the lighter colors (carbonates) and higher values in the darker colors (shales). The sample size of the dataset (nb) = 166. The correlation (R) between the predictor variable VSH and the response variable anisotropy is 0.66.

**Figure 11.**Well logs from the validation well showing a strong correlation between the cross-dipole sonic anisotropy (CXD) and the anisotropy predicted using Equation (5), and a strong correlation between the residuals and resistive fracture frequency when smoothed with a 100 ft Gaussian operator. The first track to the right of depth displays the caliper data colored by the mud cake (tan)/washout (aquamarine), along with the image quality flags (red) and acoustic quality flags (blue). The black curve, in the third track, is the volume of shale (VSH) log showing obvious lithological variability colored by VSH values, with lower values in the lighter colors (carbonates) and higher values in the darker colors (shales). The blue curve, in the fourth track, is the dipole acoustic anisotropy. The pink curve, in the fourth track, is the predicted anisotropy using Equation (6). The fifth track contains the calculated residuals in purple and the resistive fracture frequency in black.

**Figure 12.**Cross-plot from the validation well showing residuals (difference between the dipole acoustic anisotropy and the calculated anisotropy) on the (x-axis) and resistive fracture frequency (y-axis), after resampling at 25 feet. The cross-plot is colored by VSH (shale volume fraction) values, with lower values in the lighter colors (carbonates) and higher values in the darker colors (shales). The sample size of the dataset (nb) = 166. The correlation (R) between the predictor variable VSH and the response variable anisotropy is 0.9.

Mnemonic | Curve | Unit | Value Range | Sampling Rate |
---|---|---|---|---|

GMGC | Gamma Ray | API | 12.2–289.2 | 0.5 inches |

VSH | Volume of Shale | v/v | 0–1 | 0.5 inches |

ANIS | Anisotropy | % | 0.0–29.7 | 0.5 inches |

DTFS | Fast Shear Slowness | us/ft | 83.3–125.9 | 0.5 inches |

DTSS | Slow Shear Slowness | us/ft | 79.8–106.9 | 0.5 inches |

CMI_STAT | Processed Static Resistivity Image | mmho | 0–255 | 0.2 cm |

CMI_DYN | Processed Dynamic Resistivity Image | mmho | 0 -255 | 0.2 cm |

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**MDPI and ACS Style**

Walker, J.M.; Castagna, J.P.
Excess Anisotropy: A Method to Predict Frequency of Resistive Fractures in the Wolfcamp Shale. *Energies* **2023**, *16*, 2838.
https://doi.org/10.3390/en16062838

**AMA Style**

Walker JM, Castagna JP.
Excess Anisotropy: A Method to Predict Frequency of Resistive Fractures in the Wolfcamp Shale. *Energies*. 2023; 16(6):2838.
https://doi.org/10.3390/en16062838

**Chicago/Turabian Style**

Walker, Joanna M., and John P. Castagna.
2023. "Excess Anisotropy: A Method to Predict Frequency of Resistive Fractures in the Wolfcamp Shale" *Energies* 16, no. 6: 2838.
https://doi.org/10.3390/en16062838