Relationship between Hydrocarbon Generation and Reservoir Development in the Carpathian Foreland (Poland)

Matyasik, I.; Myśliwiec, M; Leśniak, G.; Such, P.

doi:10.1007/978-3-540-69426-7_22

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…Considering these controls, simulating biogenic gas generation with the tools used for modeling petroleum systems required not only a definition of the kinetics of microbial reactions, but also a reconstruction of dynamics of both the structural and thermal evolution of the sedimentary basin. Moreover, the results of earlier research [11,17,50] demonstrated that the deepest-buried Miocene sediments attained a hydrocarbon generation regime controlled by katagenetic processes. The kinetic model of thermogenic gas generation was developed based on the results of an experimental research project run in previous years at the Oil and Gas Institute, National Research Institute in Kraków ( Figure 5B) [17].…”

Section: Miocene Biogenic Gas Systemmentioning

confidence: 95%

“…Generally, the studied hydrocarbon source rocks reveal a low content of dispersed organic matter (about 0.65 wt.%, on average, rarely over 1 wt.%), represented by Type III kerogen with an admixture of Type II kerogen [50]. The degree of thermal transformation is diverse: Rocks at shallow depths are thermally immature, whereas those located at depths beneath 2500 m, particularly under the Carpathian Overthrust, reveal thermal maturity on the vitrinite reflectance scale (Ro) from 0.4 to 0.65, which correspond to Tmax values from 410 • C to 435 • C. The hydrogen index values usually fall in the range of HI = 50-200 mg HC/g TOC, but sporadically reach up to 400 mg HC/g TOC ( Figure 4) [11,17,45].…”

Section: Miocene Biogenic Gas Systemmentioning

confidence: 96%

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Miocene Biogas Generation System in the Carpathian Foredeep (SE Poland): A Basin Modeling Study to Assess the Potential of Unconventional Mudstone Reservoirs

et al. 2020

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This paper presents the results of a research project aimed at evaluating the unconventional natural gas potential of the autochthonous Miocene sediments in the Polish part of the Carpathian Foredeep. The primary objective of the study was to re-evaluate the biogenic gas generation system within Miocene sediments, paying special attention to unconventional gas resources accumulated in tight mudstone formations. The four-dimensional (4D) petroleum system modeling method (PetroMod software) was used to reconstruct the basin geometry and three-dimensional (3D) evolution through a geological timescale, in particular the progress of gas generation, migration, and accumulation processes, as well as their consequences for gas exploration and development. Special attention was paid to the dynamics of gas generation processes and the advancement of sediment compaction and their time dependence, as well as to the progress and outcomes of gas migration and accumulation processes. The results indicate significant potential for unconventional gas accumulations in mudstone reservoirs. However, part of the biogenic gas resources occurs in a dispersed form. Analysis of the dynamics of biogenic gas generation and accumulation conducted on a basin scale and within particular sedimentary complexes and depth intervals allowed an indication of the premises regarding the most favorable zones for mudstone–claystone reservoir exploration.

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Section: Miocene Biogenic Gas Systemmentioning

confidence: 95%

Section: Miocene Biogenic Gas Systemmentioning

confidence: 96%

Miocene Biogas Generation System in the Carpathian Foredeep (SE Poland): A Basin Modeling Study to Assess the Potential of Unconventional Mudstone Reservoirs

et al. 2020

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“…Other studies tried to accomplish various supervised machine learning models for prediction reservoir characterization such as permeability, porosity, shear wave, and water saturation. Additionally, Linear Multiple Regression [1,11], Random Forest (RF) [12][13][14][15], Support Vector Machines [16,17], Boosted Tree and Artificial Neural Networks (ANN) were developed to improve predicting of FZI or permeability. Tang et al (2004) [14] used statistical methods for classifying electric log facies from awest African clastic reservoir.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

Man¹,

Hien²,

Thong

et al. 2021

Energies

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The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

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“…Pyrolysis studies with the use of various techniques of analysis, monitoring, and detection of the products allow for detailed information on the quality of the source rocks in petroleum systems. Compilation studies such as Rock-Eval, thermogravimetry (TG/DTG), and Pyrolysis Gas Chromatography (Py/GC) analysis have already been initiated by the authors of this paper in the analysis of the decomposition of organic matter contained in the source rocks of the Menilite Beds [1][2][3][4], which is considered to be the main source of hydrocarbon generation in the Outer Carpathians [5][6][7][8][9][10]. The results of this study helped to clarify the generation properties of these rocks and thus to improve the simulation of generation processes in the Carpathian Basin.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of the Generation Potential of the Menilite and Istebna Beds of the Silesian Unit in the Carpathians Based on Compiled Pyrolytic Studies

et al. 2021

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The study of the source rocks was carried out with the use of various analytical methods in order to assess their generation potential and to predict the decomposition products of organic matter. The selected samples from the Menilite Beds from the Silesian and Dukla units, as well as the Istebna layers from the Silesian unit, which are classified as weak and medium source rocks in the Carpathian oil system, were examined. The generation potential and type of the products obtained from the pyrolysis of the analyzed source rocks, despite the often comparable overall content of organic matter, are significantly different. Menilite shale generated a higher abundance of hydrocarbons (alkanes, alkenes, and isoalkanes) by stage pyrolysis, which suggested that the organic matter of Menilite shale is different from the Istebna source rocks. Moreover, the thermogravimetric analysis showed a two-stage weight loss in the case of Menilite shales, while the Istebna shales were characterized by a one-stage weight loss at higher temperature. For the Istebna layers, n-alkanes from the C1–C5 range were detected as the main pyrolysis products, which proves the gas-forming type of the organic matter dispersed in these sediments. Rock-Eval analyses showed that the organic matter reached a degree of maturity corresponding to the early thermocatalytic processes (the initial oil window stage) and therefore was able to generate liquid and gaseous hydrocarbons. The comparison of the decomposition temperatures of the organic matter from the Rock-Eval and TG analyses allowed us to conclude that both measurements correlate well and can be equally used to assess the level of thermal transformations of organic matter.

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Relationship between Hydrocarbon Generation and Reservoir Development in the Carpathian Foreland (Poland)

Cited by 8 publications

References 7 publications

Miocene Biogas Generation System in the Carpathian Foredeep (SE Poland): A Basin Modeling Study to Assess the Potential of Unconventional Mudstone Reservoirs

Miocene Biogas Generation System in the Carpathian Foredeep (SE Poland): A Basin Modeling Study to Assess the Potential of Unconventional Mudstone Reservoirs

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

Differentiation of the Generation Potential of the Menilite and Istebna Beds of the Silesian Unit in the Carpathians Based on Compiled Pyrolytic Studies

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