Domain Transfer Analysis – A Robust New Method for Petrophysical Analysis
- Ravi Arkalgud (Helio Flare Limited) | Andrew McDonald (Lloyd’s Register) | Derek Crombie (Lloyd’s Register)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- SPWLA 60th Annual Logging Symposium, 15-19 June, The Woodlands, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors
- 3 in the last 30 days
- 173 since 2007
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Today, many machine learning techniques are regularly employed in petrophysical modelling such as cluster analysis, neural networks, fuzzy logic, self-organising maps, genetic algorithm, principal component analysis etc. While each of these methods has its strengths and weaknesses, one of the challenges to most of the existing techniques is how to best handle the variety of dynamic ranges present in petrophysical input data. Mixing input data with logarithmic variation (such as resistivity) and linear variation (such as gamma ray) while effectively balancing the weight of each variable can be particularly difficult to manage.
A novel method - Domain Transfer Analysis (DTA) - has been developed which uses a non-linear partial differential equation solver for predicting log curves, enabling more effective integration of disparate data types. DTA is conceived based on extensive research conducted in the field of CFD (Computational Fluid Dynamics).
This paper is focused on the application of DTA to petrophysics and its fundamental distinction from various other statistical methods adopted in the industry. Case studies are shown, predicting porosity and permeability for a variety of scenarios using the DTA method and other techniques. The results from the various methods are compared, and the robustness of DTA is illustrated. The example datasets are drawn from public databases within the Norwegian and Dutch sectors of the North Sea, and Western Australia, some of which have a rich set of input data including logs, core, and reservoir characterisation from which to build a model, while others have relatively sparse data available allowing for an analysis of the effectiveness of the method when both rich and poor training data are available.
The paper concludes with recommendations on the best way to use DTA in real-time to predict porosity and permeability. The future and ongoing applications of DTA for petrophysical analysis encompasses saturation, TOC, mineral volumes, and brittleness from the data that are available at varying stages of the drilling and completions process.
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