Száraz sûrûség meghatározása mérnökgeofizikai szondázási adatok statisztikus feldolgozásával

Felszínközeli szerkezetek kutatása során a száraz sûrûség meghatározása alapvetõ geotechnikai feladat. A jelenleg alkalmazott módszerekkel a mérési terület egy-egy pont- jában áll információ a rendelkezésünkre. A cikkben bemutatott statisztikai eljárással folytonos és in-situ információt kaphatunk a fenti mennyiség területi eloszlásáról. A szá- raz sûrûség a kõzetsûrûséggel, porozitással és agyagtartalommal áll kapcsolatban, melyet mérnökgeofizikai szondázási adatokból direkt (determinisztikus) módon határozhatunk meg. E paramétereken kívül még szükséges a víztelítettség ismerete, melyet az összes szel- vény együttes faktor analízisével határozunk meg. Az esettanulmány egy hazai területen 12 fúrásra vonatkozóan megadja a száraz sûrûség 2D eloszlását, és egy lokális regressziós összefüggést közöl a mért kõzetsûrûség és a száraz sûrûség között

Cluster Analysis Assisted Float-Encoded Genetic Algorithm for a More Automated Characterization of Hydrocarbon Reservoirs

A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological forma- tions. Conventional inversion procedures routinely used in the oil industry perform the inversion processing of borehole geophysical data locally. As having barely more types of data than unknowns in a depth, a set of marginally over-de- termined inverse problems has to be solved along a borehole, which is a rather noise sensitive procedure. For the reduc- tion of noise effect, the amount of overdetermination must be increased. To fulfill this requirement, we suggest the use of our interval inversion method, which inverts simultaneously all data from a greater depth interval to estimate petro- physical parameters of reservoirs to the same interval. A series expansion based discretization scheme ensures much more data against unknowns that significantly reduces the estimation error of model parameters. The knowledge of res- ervoir boundaries is also required for reserve calculation. Well logs contain information about layer-thicknesses, but they cannot be extracted by the local inversion approach. We showed earlier that the depth coordinates of layer- boundaries can be determined within the interval inversion procedure. The weakness of method is that the output of inversion is highly influenced by arbitrary assumptions made for layer-thicknesses when creating a starting model (i.e. number of layers, search domain of thicknesses). In this study, we apply an automated procedure for the determination of rock interfaces. We perform multidimensional hierarchical cluster analysis on well-logging data before inversion that separates the measuring points of different layers on a lithological basis. As a result, the vertical distribution of clusters furnishes the coordinates of layer-boundaries, which are then used as initial model parameters for the interval inversion procedure. The improved inversion method gives a fast, automatic and objective estimation to layer-boundaries and petrophysical parameters, which is demonstrated by a hydrocarbon field example

Shale Indicator Derived from Multivariate Statistical Analysis of Well Logs

In the paper a geostatistical approach is presented to estimate shale volume in shaly sand reservoirs. The factor analysis of well-logging data results in a new log, which correlates with the shale content of the formations. The connection between shale content and factor scores is quantified by an empirical relationship between the two variables. The nonlinear formula seems to be straight in different areas and gives consistent results both in water and hydrocarbon reservoirs. In the paper, statistical interpretation results of three different data sets originated from Hungary and the United States of America are compared. The results are verified by estimates of independent petrophysical interpretation