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The app COHIBA describes transitions between geological layers. It´s designed to describe transitions in areas where we have little information and where the uncertainty is great. This is especially true in the oil and gas reservoirs, but the technology is also suitable for describing the subsoil in areas with quick clay.

Why use COHIBA:

  • Conditions to vertical and horizontal wells using various well data: well picks, zone logs, distance data (DDR/RNS), and surface dips
  • Handles many surfaces and explicitly takes into account their internal dependencies.
  • Handles well path TVD uncertainty in multilateral horizontal wells.
  • Analyzes input data, filter away erroneous data, and reports problems.
  • Thorough analysis of model and data. Extensive reporting.
  • Handles large amount of data.
  • Stochastic depth-conversion.
  • Cross validation of wells.

COHIBA is a fast and accurate tool for making deterministic and stochastic surfaces. COHIBA can use information from:

  • Surface observations in wells (well points)
  • Horizontal well paths with zone logs
  • Seismic travel time maps
  • Interval velocity maps and models
  • Isochore maps and models
  • Spill point depth

COHIBA uses the available data in a consistent manner to minimize the uncertainty. The accuracy is further improved by linking together all surfaces in a multi-layered model.

COHIBA provides two ways of evaluating uncertainty:

  • A local depth uncertainty at every surface location can be calculated
  • Simulated (Monte Carlo) surface realizations can be generated. A set of these spans the uncertainty range

Software

Request free trial licence sek@nr.no

Metode

To model the geological transitions, we use seismic data and maps of the thickness of geological layers. Such data are uncertain, and this uncertainty is handled by describing data as multidimensional normal distributions. The uncertainty can be reduced by integrating observations of the geological layers in our models. Such observations are made in wells and the integration takes place using Bayesian statistics. By treating all data sources in a consistent way, the uncertainty in the subsoil can be minimized.
Each point in the transition between two geological layers is given by a normal distribution. Maturity in all these distributions gives the most probable representation of the subsoil. However, the uncertainty means that there are many other possible descriptions. This can be explored by subtracting the normal distributions. The true description of the subsoil will always be one of these alternative representations. We just do not know which one it is.