Improve Frac Performance with Better Stage Design
The OmniLog® lateral profile uses drilling and other existing data to map the variability of rock properties along the wellbore. Drill2Frac uses a proprietary workflow to filter data collected during drilling – including daily reports, mud motor specifications, and other pertinent information – accounting for drilling artefacts, sliding, and bad data. The resulting calculated RockMSE, provides an accurate log of rock properties that can be used to identify rock with similar susceptibility to fracturing. The OmniLog profile is then used to dramatically improve frac stage design and effective frac performance.
RockMSE can be combined with other data such as Gamma Ray (GR), mud logs, gas detector and mass spectrometry data for more detailed completion analysis.
RockMSE and OmniLog®
Drill2Frac outputs an OmniLog profile along the lateral by using an improvement to the basic MSE calculation, named RockMSE. The proprietary RockMSE computation, focuses on optimizing the quality of the input data. D2F uses accepted engineering principles, combined with quality checks by domain experts, and machine learning methods (MLM).
How is OmniLog® Used
As a proxy for rock hardness and susceptibility to fracturing, RockMSE is presented on depth with the gamma ray (GR) and mud logs obtained during drilling. The OmniLog profile identifies the locations in the wellbore, with the best probability of fracturing success.
RockMSE is uniquely related to the rock’s mechanical properties, hence it is directly related to the rock’s susceptibility to hydraulic fracturing. Used in conjunction with the organically influenced GR, reservoir driven mud logs, and mass spectrometry data, an accurate RockMSE provides compelling reasons to modify the fracturing plan. When geosteering, which maps the wellbore to the geology is available, the interpretation becomes even more valuable, since the geosteering plot helps to geologically explain and compartmentalize variations in RockMSE.
Being more accurate than the standard drilling MSE, RockMSE provides a unique, reliable measurement that can be used for optimizing frac efficiency and performance. RockMSE is therefore a close proxy for unconfined compressive strength (UCS) and provides observable insights of the near wellbore rock properties that will impact fracture efficiency.
The combination of the GR, mud log, geosteering interpretation, and OmniLog datasets, give a complete and comprehensive wellbore model, that uniquely identifies the rock properties of the lateral, enabling optimized stage design and frac performance.
The following graphic is an example of a combined dataset.
Differences between Basic Drilling MSE and RockMSE calculated in OmniLog® profile
The value of using MSE calculations based on the Teale or Pason equations, has been well established and generally used by major oil company operators as an indicator of drilling performance for over a decade. Monitoring the MSE (rock properties) trend versus the well plan has become standard drilling practice. As a result, MSE derived from the drilling data can be easily obtained from the driller’s recording services. An important advantage in the use of drilling data, is that it is stored in long-term repositories that make it possible to conduct studies of wells that have already been drilled, cased, and completed, or scheduled to be completed.
However, as noted above, there are three main uncertainties when evaluating MSE in situ:
- the specific hydraulic energy employed,
- the effect of formation hardness, and
- the loss of torque and drag along the drillstring.
These uncertainties are amplified in horizontal wells, due to friction and missing or bad data, that might have very little to do with the relative rock hardness along the wellbore, or susceptibility to fracturing. In addition, since drillers have tended to use MSE as a trending tool rather than the absolute values, recorded data might not translate directly between different wells. Therefore, the ability to normalize the recorded drilling data through references to GR, log depth, geological data, and the geosteering interpretation when available, directly impacts the accuracy of the wellbore model.
The proprietary Drill2Frac workflow mitigates the inaccuracy due to these uncertainties with a thorough quality assurance and quality checks of the input data by domain experts. This QA/QC process removes repeat data, noise, and provides an overall assessment of data quality. Where possible, lower-quality data is then improved by applying fundamental data management principles and machine learning methods (MLM). After the data has been qualified, the RockMSE is computed.
The OmniLog process is a combination of established software and expert intervention as appropriate.