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Shale Analytics

Substituting Simplifying Assumptions, Biases, and Preconcieved Notions with Hard Data and Facts.

"Hard Data" vs. "Soft Data"

“Hard Data” refers to field measurements. This is data that can readily be, and usually is, measured during the operation. For example, in hydraulic fracturing variables such as fluid type and amount, proppant type and amount, injection, breakdown and closure pressure, and injection rates are considered to be “Hard Data”. In most shale assets “Hard Data” associated with hydraulic fracturing is measured and recorded in reasonable detail and are usually available.

In the context of hydraulic fracturing of shale wells, “Soft Data” refer to variables that are interpreted, estimated or guessed. Parameters such as hydraulic fracture half length, height, width and conductivity as well as Stimulated Reservoir Volume (SRV) are are considered as “Soft Data”.

Due to its interpretive nature “Soft Data” is usually used to confirm or dispute preconcieved notions and biases during the modeling process using traditional techniques such as numerical reservoir simulation and RTA. They are used to provide confirmation for decisions that already have been made. In the context of completion design for shale wells, soft data has nothing to do with reality.

ISI's unique, proprietary, and innovative Shale Analytics technology has been applied to more than 7000 wells in Marcellus, Utica, Niobrara, Codell, Eagle Ford, Bakken, and Permian. Shale Analytics uses only "Hard Data" and is based on the augmentation of reservoir, completion, and production engineering with the state of the art Artificial Intelligence and Machine Learning. Shale Analytics technology includes three modules:

  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics

Descriptive Analytics

The complexity of oil and gas production from Shale assets is revealed rather quickly upon the use of conventional statistical analysis.

Example 1 (left): Average Maximum Injection Pressure
Example 2 (right): Injected Proppant per Stage of Hydraulic Fracturing

Frequency distribution of these two hydraulic fracturing parameters are shown below:

Let us examine the behavior of "Average Maximum Injection Pressure" and "Injected Proppant per Stage of Hydraulic Fracturing" against a production indicator such as the "Best 30 Days of Production":

Lack of any trends, patterns, or correlation between these "Hard Data" and producution from wells in this Shale asset is obvious.

Now lets use ISI's Fuzzy Pattern Recognition on the data shown above and see if there is actually a correlation between these "Hard Data" and producution that can be revealed once advance Machine Learning technology is applied.

ISI's unique and proprietary "Stairs" visualization technology uses Fuzzy Set Theory in order to categorize wells into "Poor", "Average", "Good", and "Excellent" wells based on their producution behavior. Using these new categories, "Hard Data" such as "Average Maximum Injection Pressure" and "Injected Proppant per Stage of Hydraulic Fracturing" are plotted.

Presence of a clear trend in these "Hard Data" and correlation with well qualities is unmistakable in the above figures. This trend is only revealed using ISI's "Stairs" visualization technology.

Shale PM

Presence of a clear trend in these "Hard Data" and correlation with producution is unmistakable in the figures below. These figures show the trend and pattern discovery from the original data with the maximum number of categories in each parameter. This process in Machine Learning is called "granularization". This trend is only revealed using ISI's Fuzzy Pattern Recognition technology.

Shale PM

Predictive Analytics

Data-driven predictive models are developed upon completion of detail Descriptive Analytics. The data-driven models are validated using blind data (production from wells not used during the development of the predictive model). It is ensured that the predictive model has the required generalization capabilities and honors the known physics.

Shale PM

Prescriptive Analytics

Upon completion and validation of the data-driven models, it is used for multiple purposes:

  • Production Forecasting
  • Type Curves Generation
  • Uncertainty Quantification
  • Completion Optimization
  • Well Spacing and Stacking

Development of Type Curves provide valuable information about the impact of each parameter on production. Type Curves are developed for different regions of the asset to make sure that the impact of Shale heterogeneity on the production is accounted for.

Using Monte Carlo Simulation, uncertainty analysis is performed to identify the P90-P50-P10 for new wells.

ISI's Shale Analytics Technology has a small computational footprint, such that it can be deployed on a smart phone or a tablet for fast track and on-the-fly analysis.