Application of multivariate statistical methods for characterizing heterolithic reservoirs based on wireline logs – example from the Carpathian Foreland Basin (Middle Miocene, SE Poland)

Puskarczyk, Edyta; Jarzyna, J.; Porębski, Szczepan J.

doi:10.7306/gq.1202

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…The electrofacies and the lithofacies are similar in attempting to identify and group rocks based on large-scale geologic and petrophysical features as shown by the log responses. Thus, the main purpose of the electrofacies identification is to correlate them with the lithofacies and to identify reservoir parameters related to them [13][14][15]. Process of classification and predicting electrofacies involves fuzziness at many levels, but the final desired result, facies class, is discrete.…”

Section: Introductionmentioning

confidence: 99%

Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Puskarczyk

2020

Energies

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The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C.

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Section: Introductionmentioning

confidence: 99%

Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Puskarczyk

2020

Energies

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“…Factor analysis is applicable to reduce the dimensionality of statistical problems and extract not directly measurable information from multivariate datasets (Lawley and Maxwell 1962). Puskarczyk, Jarzyna and Porebski (2015) used the PCA to reduce well-logging datasets and differentiate thin layers of sands and mudstones in middle Miocene gas reservoirs in Poland. Grana, Dvorkin and Cibin (2011) applied the method of factor analysis to effective stress prediction from seismic attributes, which gave a new possibility for estimating the abnormal pore pressure of reservoir rocks.…”

Section: Introductionmentioning

confidence: 99%

“…The principal component analysis (PCA), as a practical approximation method for solving the problem of factor analysis, has been widely used in formation evaluation. Puskarczyk, Jarzyna and Porebski (2015) used the PCA to reduce well-logging datasets and differentiate thin layers of sands and mudstones in middle Miocene gas reservoirs in Poland. Niculescu, Andrei and Ciuperca (2016) applied the same technique to separate lithostratigraphic units and delineate gas reservoirs in the Moldavian Platform.…”

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Most frequent value‐based factor analysis of direct‐push logging data

Szabó

Balogh

Stickel³

2017

Geophysical Prospecting

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An improved, iteratively re‐weighted factor analysis procedure is presented to interpret engineering geophysical sounding logs in shallow unsaturated sediments. We simultaneously process cone resistance, electric resistivity, and nuclear data acquired by direct‐push tools to give robust estimates of factor variables and water content in unconsolidated heterogeneous formations. The statistical procedure is based on the iterative re‐weighting of the deviations between the measured and calculated data using the most frequent value method famous for its robustness and high statistical efficiency. The iterative approach improves the result of factor analysis for not normally distributed data and extremely noisy measurements. By detecting a strong regression relation between one of the extracted factors and the fractional volume of water, we establish an independent method for water content estimation along the penetration hole. We verify the estimated values of water volume by using a highly over‐determined, quality‐checked interval inversion procedure. The multidimensional extension of the statistical method allows the estimation of water content distribution along both the vertical and the horizontal coordinates. Numerical tests using engineering geophysical sounding data measured in a Hungarian loessy–sandy formation demonstrate the feasibility of the most frequent value‐based factor analysis, which can be efficiently used for a more reliable hydrogeophysical characterisation of the unsaturated zone.

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“…The de velop ment of a com pre hen sive geo log i cal and non-stan dard petrophysical in ter pre ta tion may help eval u ate res er voir in ter vals in tight for ma tions. There is a need to build a co her ent meth od ology for the study of var i ous rock prop er ties and con nect ing the dif fer ent fea tures into char ac ter is tics, al low ing rock clus ter ing (Szabó, 2011;Puskarczyk et al, 2015;Jarzyna et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Digital rock models of Precambrian and Paleozoic tight formations from Poland

Krakowska

2017

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Prop er ties of se lected Pre cam brian and Pa leo zoic sed i men tary clastic rocks were analysed with re spect to their res er voir po ten tial. Mul ti di men sional anal y sis of lab o ra tory re sults and bore hole log ging data was used to con struct dig i tal mod els of pre-Me so zoic, deeply bur ied for ma tions, pres ent as tight, low-po ros ity and low-per me abil ity rocks. This mod ern sta tis ti cal and de ter min is tic ap proach as ap plied to lab o ra tory and bore hole log ging re sults worked to in te grate data at dif fer ent scales. The re sults ob tained are use ful not only in fur ther sci en tific re search but also found a use in in dus trial ap pli ca tion. As a first step, sta tis ti cal meth ods, in clud ing clus ter ing and sep a ra tion of ho mo ge neous groups, en abled dig i tal rock model cre ation on the ba sis of the re sults of such lab o ra tory mea sure ments as pycnometry, mer cury porosimetry, nu clear mag netic res onance spec tros copy or com puted X-ray to mog ra phy. Next, the mod els con structed were ap plied in bore hole log ging in terpre ta tion to find in ter vals with sim i lar petrophysical prop er ties within the group and dif fer ent prop er ties be tween the groups. This ap proach al lowed im ple men ta tion of upscaling pro ce dures of lab o ra tory ex per i ments at mi cro-and nano-scale to borehole log ging scale. High cor re la tions were es tab lished be tween the log petrophysical pa ram e ters within the dig i tal mod els. This ap proach can be used to di vide the suc ces sion cored into in ter vals with dif fer ent petrophysical pa ram e ters.Key words: dig i tal rock mod els, Pre cam brian and Pa leo zoic sed i men tary rocks, petrophysical pa ram e ters, sta tis ti cal anal ysis of petrophysical data.

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Application of multivariate statistical methods for characterizing heterolithic reservoirs based on wireline logs – example from the Carpathian Foreland Basin (Middle Miocene, SE Poland)

Cited by 9 publications

References 8 publications

Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Most frequent value‐based factor analysis of direct‐push logging data

Digital rock models of Precambrian and Paleozoic tight formations from Poland

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