Stratigraphic correlation uncertainty : On the impact of the sediment transport direction in computer-assisted multi-well correlation

@phdthesis{baville:tel-03945227,
 abstract = {Subsurface modeling is a way to predict the structure and the connectivity of stratigraphic units by honoring subsurface observations. These observations are commonly be sampled along wells at a large and sparse horizontal scale (kilometer-scale) but at a fine vertical scale (meter-scale). There are two types of well data: (1) well logs, corresponding to quasi-continuous (regular sampling) geophysical measurements along the well path (e.g., gamma ray, sonic, neutron porosity), and (2) regions, corresponding to categorical reservoir properties and defined by their top and bottom depths along the well path (e.g., biozones, structural zones, sedimentary facies). Markers are interpreted along the well path and can be associated in order to generate a consistent set of marker associations called well correlations. These well correlations may be generated manually (deterministic approach) by experts, but this may be prone to biases and does not ensure reproducibility. Well correlations may also be generated automatically (deterministic or probabilistic approach) by computing with an algorithm a large number of consistent well correlations and by ranking these realizations according to their likelihood. The likelihood of these computer-assisted well correlations are directly linked to the principle of correlation used to associate markers. This work introduces two principles of correlation, which tend to reproduce the chronostratigraphy and the depositional processes at the parasequence scale: (1) "a marker (described by facies and distality taken at the center of an interval having a constant facies and a constant distality) cannot be associated with another marker described by a depositionally deeper facies at a more proximal position, or a depositionally shallower facies at a more distal position", and (2) "the lower the difference between a chronostratigraphic interpolation (in between markers) and a conceptual depositional profile, the higher the likelihood of the marker association". These two principles of correlation are first benchmarked with analytical solutions and applied on synthetic cases. They have then been used (1) to predict the connectivity of stratigraphic units from well data without strong knowledge on depositional environments by inferring the correlation parameters, or (2) to evaluate the likelihood of a hypothetical depositional environment by generating stochastic realizations and assessing the uncertainties. The methods are applied on a siliciclastic coastal deltaic system targeting a Middle Jurassic reservoir in the South Viking Graben in the North Sea.This work enables (1) to define two specific principles of correlation defined by a few parameters that can be used to generate stochastically well correlations within coastal deltaic systems, and (2) to open the path towards a simple combination of specific principles of correlation to obtain a better characterization of coastal deltaic systems by assessing the uncertainties.},
 author = {Baville, Paul},
 hal_id = {tel-03945227},
 hal_version = {v1},
 keywords = {Multi-Well correlations ; Sequence stratigraphy ; Sediment transport direction ; Coastal sedimentary depositional environments ; Uncertainty assessment ; Dynamic Time Warping Algorithm ; Corr{\'e}lations multi-puits ; Stratigraphie s{\'e}quentielle ; Direction de transport de s{\'e}diment ; Environnements de d{\'e}p{\^o}ts s{\'e}dimentaires c{\^o}tiers ; Gestion d'incertitudes ; Algorithme de D{\'e}formation Temporelle Dynamique},
 month = {April},
 number = {2022LORR0111},
 pdf = {https://hal.univ-lorraine.fr/tel-03945227/file/DDOC_T_2022_0111_BAVILLE.pdf},
 school = {{Universit{\'e} de Lorraine}},
 title = {{Stratigraphic correlation uncertainty : On the impact of the sediment transport direction in computer-assisted multi-well correlation}},
 type = {Theses},
 url = {https://hal.univ-lorraine.fr/tel-03945227},
 year = {2022}
}