COHIBA 5.5  Surface Modelling and Depth Conversion
COHIBA 5.5  Surface Modelling and Depth Conversion
COHIBA Version 5.5 was released September 8^{th} 2017. Important improvements are:

The handling of well data uncertainty has been significantly improved. There is now a clear distinction between surface uncertainty, well point pick uncertainty, and well path positioning uncertainty. Surfaces and uncertain well paths will now intersect at well points if the well point pick uncertainty is zero. The vertical positioning uncertainty in the well is called well path TVD uncertainty and the uncertainty in the interpretaion of zone log transitions along the well path is called well point MD pick uncertainty. The vertical projection of the pick uncertainty is called well point TVD pick uncertainty. The specification of well uncertainty has been simplified as a consequence of this.
 Several elements have changed names for two reasons: To reflect a more consistent terminology and to reflect a simpler specification of well uncertainty. Legacy element names will still work but the user is encouraged to update the element names.
 Many output files have been given new names. Also the output file structure has been reorganized so that the important files are simpler to find among the numerous output files.
 Many small errors have been corrected.
For a complete list of all modifications please consult the release notes in the user manual.
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 multilayered 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
The main advantages of COHIBA are:
 Ability to condition to horizontal wells
 Handles many surfaces and explicitly takes into account their interdependencies
 Handles erosions, pinchouts, thin layers, and onlapping surfaces
 QC and robustness. COHIBA analyze all input data, filter away erroneous data, and reports possible problems
 Can handle well path TVD uncertainty in multilateral horizontal wells and modify the well depth to obtain consistency with surfaces and other wells
For details and examples please have a look at the COHIBA user manual:
Conditioning to well points versus conditioning to well paths
Below are two cross sections showing the improvements obtained by conditioning the surfaces to well paths in addition to the well points. The left figure is obtained using only well points while the right picture is obtained using both well points and well paths. Note how all surfaces are modified to obtain consistent and realistic zonation.
Below is a second example. Again we see how COHIBA modifies all surfaces to make a consistent and realistic zonation.
Uncertainty reduction
The following picture shows the result of conditioning to well paths. We clearly see how the surfaces are accurately determined along the well paths.
The lefthand pictures show the results from using well points whereas the righthand pictures show the results from using both well points and the well paths. The uncertainty is significantly reduced along the well paths because the well trajectories are confined to very thin zones similar to the situations in the cross sections above.
Geosteering
The animation below simulates the use of distance data acquired from deep directional resistivity (DDR) logs during a drilling process, and shows a vertical cross section along a planned well trajectory. The reservoir consists of a top and a base surface with the reservoir zone in red. The planned trajectory is shown as a continuous line entering the top of the anticline and passing through the reservoir. The actually drilled trajectory starts to deviate from the planned well as the true distances to the top and base reservoir are updated. The dashed lines represent the surface uncertainty envelopes and shrinks as more data becomes available.
Publications
 (2015) Surface prediction using rejection sampling to handle nonlinear constraints Bulletin of Canadian Petroleum Geology, December 2015, v. 63, p. 304317,

Line Plouman Jensen, Emil StürupToft, Scott Pearse, and Adam Cherrett (2015). An integrated approach to geostatistical depth conversion and gross rock volume estimation, 3(1), SC9SC17, doi: 10.1190/INT20140030.1
 P. Abrahamsen (2005), "Combining Methods for Subsurface Prediction", in O. Leuangthong and C.V. Deutsch (eds.) "Geostatistics Banff 2004", Vol. 2, Springer, Dordrecht, pp. 601610
 P. Abrahamsen and F.E. Benth (2001), "Kriging with Inequality Constraints", Math. Geol. Vol. 33 No. 6, pp. 719744.
 Petter Abrahamsen, Ragnar Hauge, Knut Heggland, Petter Mostad (2000), "Estimation of Gross Rock Volume of Filled Geological Structures With Uncertainty Measures", SPE Reservoir Evaluation & Engineering, Vol 3, No. 4, pp. 304  309, doi: 10.2118/65419PA
 P. Abrahamsen (1993), "Bayesian Kriging for Seismic Depth Conversion of a Multilayer Reservoir" in A. Soares (ed.) "Geostatistics Troia '92". Kluwer Academic Publ., Dordrecht, pp. 385398
 H. Omre (1987), "Bayesian Kriging  Merging Observations and Qualified Guesses in Kriging" Math. Geol. Vol. 19, No. 1, pp. 2539. doi: 10.1007/BF01275432
 Vigsnes, M., Kolbjørnsen, O., Hauge, V.L. Hauge, P. Dahle and P. Abrahamsen (2017), "Fast and Accurate Approximation to Kriging Using Common Data Neighborhoods" Math Geosci 49: 619. https://doi.org/10.1007/s1100401696657
Conference contributions:
 P. Dahle, P. Abrahamsen, A. AlmendralVazquez (2015), Simultaneous prediction of geological surfaces and well paths. Conference, Petroleum Geostatistics, Biarritz, France, September 7–11, 2015. Full text.
 Petter Abrahamsen, Pål Dahle, Vera Louise Hauge, Ariel AlmendralVazquez and Maria Vigsnes, (2014) Surface prediction using rejection sampling to handle nonlinear relationships, presented at the 2014 Gussow Geosciences Conference, September 22.24., 2014, Banff, Canada, pdf,
 A. Mannini and S. Pearse (2014), How Big an Elephant Can Be, presented at 76th EAGE Conference and Exhibition, Amsterdam June 2014, doi: 10.2118/159746MS
 K. B. Neumann, B. K. Hegstad, E. Bratli, I. K. Osmundsen (2012), Uncertainty study on inplace volumes in Statoil, presented at Ninth International Geostatistics Congress, Oslo, Norway, June 11 – 15, 2012.
 Abrahamsen, Petter; Dahle, Pål; Skorstad, Arne (2012). A Fast and Consistent Geostatistical Approach for Constraining 3D Structural Models to Horizontal Wells. presented at EAGE workshop Integrated Reservoir Modelling  Are we doing it right?, 25.28. November 2012, Dubai, Fulltext
 V.R. Stenerud, H. Kallekleiv, P. Abrahamsen, P. Dahle, A. Skorstad, M.H. Aalmen Viken, Added Value by Fast and Robust Conditioning of Structural Surfaces to Horizontal Wells for RealWorld Reservoir Models, SPE Annual Technical Conference and Exhibition, 810 October 2012, San Antonio, Texas, USA, doi: 10.2118/159746MS
 Petter Abrahamsen, Pål Dahle, Frode Georgsen and Arne Skorstad (2010), "A Consistent Geostatistical Approach for Constraining Multiple Surfaces to Horizontal Wells", poster presented at GEO 2010, 9th Middle East Geoscience Conference and Exhibition, 7  10 March 2010, Manama, Bahrain
 P. Abrahamsen and H. Omre (1994), "Random Functions and Geological Surfaces", in proceedings "ECMOR IV, 4th European Conference on the Mathematics of Oil Recovery, Røros, Norway, 710 June 1994.
Institute reports:
 P. Abrahamsen (1999), "FFT algorithm for simulating Gaussian random fields", Report, NRnote SAND/10/99, 28 pages
 P. Abrahamsen (1996), "Geostatistics for Seismic Depth Conversion", Report, NRnote SAND/06/1996, 9 pages.
Project period
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