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Multiscale evaluation of a thin-bed reservoir

Lis-Śledziona Anita

Geology, Geophysics and Environment

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2021

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Vol. 47, no. 1

5--20

EN

A thin-bed laminated shaly-sand reservoir of the Miocene formation was evaluated using two methods: high resolution microresistivity data from the XRMI tool and conventional well logs. Based on high resolution data, the Earth model of the reservoir was defined in a way that allowed the analyzed interval to be subdivided into thin layers of sandstones, mudstones, and claystones. Theoretical logs of gamma ray, bulk density, horizontal and vertical resistivity were calculated based on the forward modeling method to describe the petrophysical properties of individual beds and calculate the clay volume, porosity, and water saturation. The relationships amongst the contents of minerals were established based on the XRD data from the neighboring wells; hence, the high-resolution lithological model was evaluated. Predicted curves and estimated volumes of minerals were used as an input in multimineral solver and based on the assumed petrophysical model the input data were recalculated, reconstructed and compared with the predicted curves. The volumes of minerals and input curves were adjusted during several runs to minimalize the error between predicted and recalculated variables. Another approach was based on electrofacies modeling using unsupervised self-organizing maps. As an input, conventional well logs were used. Then, the evaluated facies model was used during forward modeling of the effective porosity, horizontal resistivity and water saturation. The obtained results were compared and, finally, the effective thickness of the reservoir was established based on the results from the two methods.

2

Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

Puskarczyk Edyta

Acta Geophysica

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2019

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Vol. 67, no. 6

1991--2003

EN

Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.

3

Wyznaczenie elektrofacji w sweet spotach sylursko-ordowickich formacji łupkowych z rejonu basenu bałtyckiego na podstawie profilowań geofizyki otworowej z zastosowaniem samoorganizujących się sieci neuronowych

Wawrzyniak-Guz K., Krakowska P., Puskarczyk E., Jarzyna J.

Prace Naukowe Instytutu Nafty i Gazu

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2016

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nr 209 wyd. konferencyjne

589--593

PL

Utwory formacji łupkowych charakteryzują się znaczną zmiennością potwierdzoną licznymi analizami. W celu scharakteryzowania zmienności dwóch sweet spotów z obszaru basenu bałtyckiego – ogniwa Jantaru i formacji z Sasina, wydzielono w otworze L-1 elektrofacje na podstawie standardowego zestawu profilowań geofizyki otworowej. Do obliczeń wykorzystano analizę IPSOM korzystającą z samoorganizujących się sieci neuronowych opartych na indeksowanych mapach Kohonena.

EN

Shale formations are characterized with significant heterogeneity, which is confirmed by numerous analyses. In order to describe the variability of the two sweet spots from the Baltic Basin, i.e. the Jantar Member and Sasino Formation, the electrofacies were determined on the basis of the set of standard well logs recorded in the L-1 well. The calculations were done with the use of the IPSOM analysis that is based on indexed Kohonen maps (unsupervised neural network technology).

4

Charakterystyka elektrofacji na podstawie profilowań geofizyki otworowej, z wykorzystaniem wielowymiarowych metod statystycznych

Baudzis S., Szuniewicz Z., Puskarczyk E., Jarzyna J.

Prace Naukowe Instytutu Nafty i Gazu

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2016

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nr 209 wyd. konferencyjne

745--748

PL

W pracy przedstawiono analizę profilowań geofizyki otworowej w kilku otworach z rejonu zapadliska przedkarpackiego, pod kątem wydzielenia elektrofacji. Korzystając z oprogramowania firm Paradigm i Schlumberger wykonano analizy wykorzystujące wielowymiarowe metody statystyczne, m.in. analizę skupień i algorytm oparty na sztucznych sieciach neuronowych. W efekcie uzyskano podział analizowanego interwału na jednorodne grupy – elektrofacje. Rezultaty dowiązano do wyników standardowej interpretacji profilowań geofizyki otworowej oraz wykonano korelację międzyotworową.

EN

Electrofacies analysis was performed for several wells in the Carpathian Foredeep region. Paradigm and Schlumberger software were used for analysis and multivariate statistical methods were applied e.g. cluster analysis and neural networks algorithms. As a result electrofacies diversification of analyzed intervals was achieved. Results were tied to well logging interpretation and a well cross-correlation was done.