Research-Derived Insights into Surface Geochemical Hydrocarbon Exploration

Mud volcanoes in Grobogan are referred as the Grobogan Mud Volcanoes Complex in Central Java where there is evidence of oil seepages. This comprehensive research is to determine the characteristics and hydrocarbon potential of the mud volcanoes in the Central Java region as a new opportunity for hydrocarbon exploration. The Grobogan Mud Volcano Complex consists of eight mud volcanoes that have its characteristics based on the study used the geological surface data and seismic literature as supporting data on eight mud volcanoes. The determination of geological surface characteristics is based on geomorphological analysis, laboratory analysis such as petrography, natural gas geochemistry, water analysis, mud geochemical analysis and biostratigraphy. Surface data and subsurface data are correlated, interpreted, and validated to make mud volcano system model. The purpose of making the mud volcanoes system model is to identify the hydrocarbon potential in Grobogan. This research proved that each of the Grobogan Mud Volcanoes has different morphological forms. Grobogan Mud Volcanoes materials are including muds, rock fragments, gas, and water content with different elemental values. Based on this research result, there are four mud volcano systems models in Central Java, they are Bledug Kuwu, Maesan, Cungkrik, and Crewek type. The source of the mud is from Ngimbang and Tawun Formation (Middle Eocene to Early Miocene) from biostratigraphy data and it been correlated with seismic data. Grobogan Mud Volcanoes have potential hydrocarbons with type III kerogen of organic matter (gas) and immature to early mature level based on TOC vs HI cross plot. The main product are thermogenic gas and some oil in relatively small quantities. Water analysis shows that it has mature sodium chloride water. This analysis also shows the location was formed within formations that are deposited in a marine environment with high salinity. Research of mud volcanos is rarely done in general. However, this comprehensive research shows the mud volcano has promising hydrocarbon potential and is a new perspective on hydrocarbon exploration.

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Oil to Source Rock Correlation Using Biomarker Data Through Pattern Matching and Fingerprinting Analysis in “Kitkat” Field, Jabung Block, South Sumatra Basin

Proc. Indon. Petrol. Assoc., Digital Technical Conference, 2020 ◽

10.29118/ipa20-sg-251 ◽

2020 ◽

Author(s):

S., R. Muthasyabiha

Keyword(s):

Pattern Matching ◽

Source Rock ◽

Vitrinite Reflectance ◽

Hydrocarbon Exploration ◽

Source Rocks ◽

Maturity Level ◽

Fingerprinting Analysis ◽

Kerogen Type ◽

Oil Characteristics ◽

Biomarker Data

Geochemical analysis is necessary to enable the optimization of hydrocarbon exploration. In this research, it is used to determine the oil characteristics and the type of source rock candidates that produces hydrocarbon in the “KITKAT” Field and also to understand the quality, quantity and maturity of proven source rocks. The evaluation of source rock was obtained from Rock-Eval Pyrolysis (REP) to determine the hydrocarbon type and analysis of the value of Total Organic Carbon (TOC) was performed to know the quantity of its organic content. Analysis of Tmax value and Vitrinite Reflectance (Ro) was also performed to know the maturity level of the source rock samples. Then the oil characteristics such as the depositional environment of source rock candidate and where the oil sample develops were obtained from pattern matching and fingerprinting analysis of Biomarker data GC/GCMS. Moreover, these data are used to know the correlation of oil to source rock. The result of source rock evaluation shows that the Talangakar Formation (TAF) has all these parameters as a source rock. Organic material from Upper Talangakar Formation (UTAF) comes from kerogen type II/III that is capable of producing oil and gas (Espitalie, 1985) and Lower Talangakar Formation (LTAF) comes from kerogen type III that is capable of producing gas. All intervals of TAF have a quantity value from very good–excellent considerable from the amount of TOC > 1% (Peters and Cassa, 1994). Source rock maturity level (Ro > 0.6) in UTAF is mature–late mature and LTAF is late mature–over mature (Peters and Cassa, 1994). Source rock from UTAF has deposited in the transition environment, and source rock from LTAF has deposited in the terrestrial environment. The correlation of oil to source rock shows that oil sample is positively correlated with the UTAF.

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GEOLOGY AND HYDROCARBON EXPLORATION POTENTIAL OF BELIZE

10.1130/abs/2018am-321689 ◽

2018 ◽

Author(s):

Jamin Lopez ◽

◽

M. Royhan Gani ◽

Nahid D. Gani

Keyword(s):

Hydrocarbon Exploration

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Hydrocarbon Exploration and Biostratigraphy: The Application of Sea-level Studies

Sea Surface Studies ◽

10.1007/978-94-015-1146-9_18 ◽

1987 ◽

pp. 531-568 ◽

Cited By ~ 1

Author(s):

R. J. N. Devoy

Keyword(s):

Sea Level ◽

Hydrocarbon Exploration

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Probability elicitation using geostatistics in hydrocarbon exploration

Computational Geosciences ◽

10.1007/s10596-021-10084-9 ◽

2021 ◽

Cited By ~ 2

Author(s):

André Luís Morosov ◽

Reidar Brumer Bratvold

Keyword(s):

Value Creation ◽

Decision Process ◽

Conceptual Models ◽

Joint Probability ◽

Decision Makers ◽

Hydrocarbon Exploration ◽

Joint Probability Distribution ◽

Proof Of Concept ◽

Geological Information ◽

Decision Supporting

AbstractThe exploratory phase of a hydrocarbon field is a period when decision-supporting information is scarce while the drilling stakes are high. Each new prospect drilled brings more knowledge about the area and might reveal reserves, hence choosing such prospect is essential for value creation. Drilling decisions must be made under uncertainty as the available geological information is limited and probability elicitation from geoscience experts is key in this process. This work proposes a novel use of geostatistics to help experts elicit geological probabilities more objectively, especially useful during the exploratory phase. The approach is simpler, more consistent with geologic knowledge, more comfortable for geoscientists to use and, more comprehensive for decision-makers to follow when compared to traditional methods. It is also flexible by working with any amount and type of information available. The workflow takes as input conceptual models describing the geology and uses geostatistics to generate spatial variability of geological properties in the vicinity of potential drilling prospects. The output is stochastic realizations which are processed into a joint probability distribution (JPD) containing all conditional probabilities of the process. Input models are interactively changed until the JPD satisfactory represents the expert’s beliefs. A 2D, yet realistic, implementation of the workflow is used as a proof of concept, demonstrating that even simple modeling might suffice for decision-making support. Derivative versions of the JPD are created and their effect on the decision process of selecting the drilling sequence is assessed. The findings from the method application suggest ways to define the input parameters by observing how they affect the JPD and the decision process.

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