BASINWORX multi-well evaluation

BASINWORX multi-well evaluation employs artificial neural network (ANN) modeling and other artificial intelligence techniques and machine learning techniques to develop data-driven models that identify factors that optimize production in fractured completions. The key indicators of reservoir performance are used for the optimization of frac and completion designs.

Multi-well data for reservoir-based evaluation BASINWORX process is a data-driven modeling approach that integrates multidisciplinary data into a predictive model. To create a robust model for data analysis and forecasting, information from 20+ wells is required.

  • Wellbore diagram with tubing and casing size, depths, perforation location, etc.
  • Completion report including fracture staging, treatment volumes, rates and pressures
  • Drilling data including mud logs, ROP, mud weight, directional survey, geologic survey, etc.
  • Geologic data including TVD, isopach, mapped reservoir characteristics, etc.
  • Daily production and producing pressures
  • Economic evaluation requires completion and frac costing data

Identify key factors influencing performance

Proprietary models are built from completion and geology data types to predict new well production. The BASINWORX modeling process includes:

  • Build a predictive model by incorporating multidisciplinary data and artificial intelligence modeling technique
  • Perform sensitivities on the model to quantify the impact of each predictor and rank significance
  • Evaluate well performance using alternative completion and frac designs
  • Discover hidden correlations within the data using advanced multivariate statistical data evaluation

Optimize well performance

BASINWORX evaluations provide an index of the critical reservoir, drilling, fracturing and completion factors that drive productivity in the field.

Project deliverables

  • Production forecasts and economics
  • Key impact parameter identification
  • Alternative completion and frac design recommendations
  • New completion procedures benchmarking
  • High-grade drilling location identification
  • Project model and database

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