Facies delineation from log data using the Ipsom technology
- To investigate data structure inherent in the log data set using “unsupervised” ordering and data partitioning methods.
- To perform an initial indexation of the data partitions using the raw core descriptions. Quantify the goodness of fit between raw core descriptions and the resulting predicted facies curve using the Ancor tool for correspondence analysis (observed vs. predicted data analysis mode).
- To refine the correspondence of the given geological partitions and observed log data partitions by reference to data viewers and log displays; by this means the user keeps control of the interpretation and need not accept just the statistically based model. Derive an optimal representation of the core facies data within the log data set by iterative adaptation of the core descriptions.
- To propagate the final indexed log predictions based on the refined model into test wells.
To achieve the most robust prediction models, Techsia uses the Ipsom module and the Ancor tool.
Parameter: prediction (K, phi etc.) and data repair using K.mod
- To develop one or more neural network models as appropriate for predicting (inter alia) the formation permeability and porosity distribution. Classic techniques such as Linear Discriminant Analysis can provide guidance on the best log data to use to predict any particular parameter of choice from a network model. The quantitative assessment of the quality of the network prediction and the management of the associated uncertainties is available by use of a combination of technologies (the indexed and probabilised self organising map and the multi-layer perceptron).
- To model log responses in intervals affected by eg. poor borehold quality, back propagation NN are used to utilize all available data. Automatic patching of good and bad data provides an efficient and easy-to-use solution.
For high-end results, Techsia uses the K.mod module.
"Conventional" log interpretation with uncertainty analysis
- To evaluate log data for (inter alia) lithology, porosity and fluid saturation using conventional log interpretation algorithms as appropriate. Interpretation may be by sequential analysis or simultaneous probabilistic analysis of various available log data.
- To investigate model uncertainties through Monte Carlo analysis and inversion.
To achieve clear results and appreciate the uncertainties, Techsia uses the Quanti module.
Hydraulic units elicitation
- To delineate “hydraulic units” through evaluation of (inter alia) permeability, porosity and lithology characteristics (e.g. volume of shale in clastic formations). To integrate fully to log and core domains by involving available capillary pressure (Pc) data to the maximum extent.
- The partitioning of the formation according to petrophysical characteristics is established by applying the indexed and probabilised self organizing map to these derived logs. Saturation may be a useful arbiter curve in this process.
- To refine the observed petrophysical class partitions. Multivariate data displays and full interactivity between processes provide clear insight to the user.
For the most consistent interpretation, Techsia uses the Ipsom module.
Analysis of correspondences
- To quantify the goodness of fit between the adapted core descriptions and the final facies prediction using the “observed vs. predicted” data analysis mode.
- To quantify the relationships between the log-based geological facies prediction and the petrophysical classes of formation characteristics using the “Lithofacies vs Petrofacies” data analysis mode. This will provide associative probability distributions that link log-derived facies to the occurrence of hydraulic units.
To provide unique insights into the results, Techsia uses the Ancor tool.
ReportingTo get explanatory compilation of methods and results and to export digital quantitative results.