NDA@SZTAKI

Well Log Analysis by Global Optimization-based Interval Inversion Method

Dobróka, Mihály

SzabĂł, Norbert PĂ©ter

Cranganu, Constantin

Luchian, Henri

Breaban, Mihaela Elena

Springer International Publishing AG Switzerland

2015

QE Geology / földtudományok

QE01 Geophysics / geofizika

Artificial intelligence methods play an important role in solving an
optimization problem in well log analysis. Global optimization procedures such as
genetic algorithms and simulated annealing methods offer robust and highly
accurate solution to several problems in petroleum geosciences. According to
experience, these methods can be used effectively in the solution of well-logging
inverse problems. Traditional inversion methods are used to process the borehole
geophysical data collected at a given depth point. As having barely more types of
probes than unknowns in a given depth, a set of marginally over-determined inverse
problems has to be solved along a borehole. This single inversion scheme represents
a relatively noise-sensitive interpretation procedure. For the reduction of
noise, the degree of over-determination of the inverse problem must be increased.
To fulfill this requirement, the so-called interval inversion method is developed,
which inverts all data from a greater depth interval jointly to estimate petrophysical
parameters of hydrocarbon reservoirs to the same interval. The chapter gives a
detailed description of the interval inversion problem, which is solved by a series
expansion-based discretization technique. Different types of basis functions can be
used in series expansion depending on the geological structure to treat much more
data against unknowns. The high degree of over-determination significantly
increases the accuracy of parameter estimation. The quality improvement in the
accuracy of estimated model parameters often leads to a more reliable calculation of
hydrocarbon reserves. The knowledge of formation boundaries is also required for
reserve calculation. Well logs do contain information about layer thicknesses,
which cannot be extracted by the traditional local inversion approach. The interval
inversion method is applicable to derive the layer boundary coordinates and certain
zone parameters involved in the interpretation problem automatically. In this
chapter, it is analyzed how to apply a fully automated procedure for the determination
of rock interfaces and petrophysical parameters of hydrocarbon formations.
Cluster analysis of well-logging data is performed as a preliminary data processing step before inversion. The analysis of cluster number log allows the separation of
formations and gives an initial estimate for layer thicknesses. In the global inversion
phase, the model including petrophysical parameters and layer boundary coordinates
is progressively refined to achieve an optimal solution. The very fast simulated
re-annealing method ensures the best fit between the measured data and
theoretical data calculated on the model. The inversion methodology is demonstrated
by a hydrocarbon field example, which shows an application for shaly sand
reservoirs. The theoretical part of the chapter gives a detailed mathematical formulation
of the inverse problem, while the case study focuses on the practical
details of its solution by using artificial intelligence tools.

angol

Book Section

PeerReviewed

info:eu-repo/semantics/bookPart

text

http://real.mtak.hu/26993/1/Springer_chapter_2015.pdf

Dobróka, Mihály and Szabó, Norbert Péter (2015) Well Log Analysis by Global Optimization-based Interval Inversion Method. In: Artificial Intelligent Approaches in Petroleum Geosciences. Springer International Publishing AG Switzerland, pp. 245-268. ISBN 978-3-319-16530-1

http://real.mtak.hu/26993/