Fracture Identification with D-Code

Fractures encountered while drilling can cause added complexity to any wellbore evaluation by leading to zones of mud loss, pathways for frac fluid, and zones of increased production. Identifying the location of these fractures allows for more informed decisions and is an integral part of wellbore design, casing location selection, mud system preparedness planning, perforation placement, and completion design. Core Laboratories D-Code? technology easily identifies fractures by leveraging the information collected while drilling, minimizing unexpected costs in drilling operations as the result of inaccurate predictions.

Drilling data can be a valuable tool to identify fractures that can be pulled from any vendor source. After data acquisition, the first step is a rigorous quality control process to remove erroneous data by calculating the derivatives of the block height allowing quick and consistent identification of the connection points. The erroneous data can then be flagged and removed from the drilling logs. This quality control process eliminates spikes in the data associated with the initiation of drilling.

Figure 1: Example of the initial review of the drilling data. At this stage, we identify the locations of connections to ensure that erroneous data will be removed before modeling.

A detailed review of the drilling reports is then conducted to investigate any additional spikes in the data or to note any incidences of rig communication problems, system outages, tool failures, or any other data that is unrelated to the bit interacting with the rock. After completing the initial QC process, all available data is evaluated to ensure that it is on depth by shifting the mud logs, MWD or LWD logs, and drilling logs to the wireline data - or if wireline data is not available, shifting the mud logs and drilling logs to the MWD. Depth correlation of logged data (in any form) is crucial to the accuracy of the developed models and quality control of the outputs.

Geomechanical properties are generated through core-calibrated transforms by leveraging the captured drilling data to perform physics-based force calculations. A few of the key input parameters are weight on bit (WOB), rate of penetration (ROP), revolutions per minute (RPM), torque (Ctor), bit spec information, and mud motor operations. These inputs are combined to identify the forces needed to remove various amounts of rock on the half-foot scale. Unique signatures (figure 2) are identified through sharp spikes in the drilling outputs. When the bit encounters fractures in the rock, spikes in the Poisson's Ratio and Young's Modulus can easily be identified as events.

The interpretation of these events can then be evaluated and identified by reviewing the drilling reports to confirm that they are not caused by rig activity. The identification of the fractures can then be noted in both vertical and lateral wellbores for better understanding the rock for completions, drilling hazards, mud loss, and numerous other applications.

Figure 2: Example of fracture identification in a lateral wellbore.

The above figure shows the application of the drilling data to identify fractures in the lateral. The contrast of signatures identifies a drilling event caused by drilling operations.

Beyond fracture identification, D-Code's? drilling data evaluation generates geomechanical properties of the rock.?Identifying the changes in rock fabric and other drilling events allows for improved planning and correlation between wellbores in addition to improving future stimulation planning, casing point selection, and potential hazard identification.

Figure 3: Lateral example of fracture identification with microseismic data. Stars identify microseismic location of fractures. Red shading of the brittleness show fractures identified by D-Code.?

Fracture identification and D-Code? can be paired with additional data to understand the encountered rock and the surrounding properties.

Pairing tracer data with D-Code provides further knowledge of how the rock will behave when stimulated. The results could lead to changes in fluids used, stage spacing, or identification of rock types to avoid placing perforations.

By pairing with microseismic, locations of fractures can be confirmed from an independent source, allowing for greater confidence in the drilling-based identification method. Pairing with core data provides geomechanical model validation, insight into the causes of geomechanical variability, and a projection of the vertical core-based petrophysical models along the lateral. Targeted cuttings analysis can also be performed for additional insight into the causes of geomechanical properties variation in a lateral or un-cored section.

In this digital transition, data accumulation is an ever-increasing burden that geoscientists must overcome to harness the power within the data. Drilling data alone is often very underutilized due to the overwhelming amount of data collected. D-Code? offers the ability to fully use data collected on nearly every well. It connects multiple data sources, delivers detailed visualizations, and correlates with other data sets providing insight into contributing factors of variations along the wellbore.

D-Code? provides the key to deciphering any well, either as a standalone analysis to identify fractures and geomechanical properties or as the data-spine providing correlative support among rock, tracer, and production data.