Siliciclastic depositional model within a subsurface coastal parasequence based on barriers and baffles – An application to the Donghe Sandstone Member in the Hudson Oilfield, Tarim Basin, NW China

Sun, Panke; Fang, Huijing; Zhang, Xu; Wang, Chao; Xu, Huaimin; Lu, Man

doi:10.1016/j.petrol.2020.107947

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…4b). Previous studies (Sun et al, 2020;Lei et al, 2019Lei et al, , 2021Li et al, , 2023 suggest that these sandstones deposits originate from wave-dominated shoreface environments. In the Donghetang area, the Donghe sandstones have a thickness of approximately 250 meters, with a buried depth ranging from 5700 to 6000 meters.…”

Section: Marine Sandstonesmentioning

confidence: 82%

Sedimentary stratigraphic uncertainty: A quantitative analysis framework based on information theory and stochastic processes

Lei,

Yan,

et al. 2024

Preprint

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Sedimentary stratigraphic sequences are crucial archives of Earth's geological history, providing significant insights into paleoenvironments, climate changes, tectonic activities, and hydrocarbon reservoirs. However, the complexity of subsurface conditions and incomplete knowledge often introduce substantial uncertainty into stratigraphic interpretations. This paper proposes a comprehensive framework for quantifying, communicating, and analyzing stratigraphic uncertainty by incorporating principles from information theory and stochastic processes. Our methodology integrates Markov chains, Poisson processes, and Markov pure-jump processes to mathematically represent the stochastic nature of stratigraphic units, boundaries, and sequences. We also formulate entropy models aligned with these stochastic processes, establishing a robust foundation for addressing uncertainty. Through detailed case studies across diverse sedimentary environments—such as marine sandstones, braided river deltas, and meandering river systems—our findings reveal several key insights: (1) Stratigraphic states within a sequence can be accurately predicted using the Markov chain model, with entropy and entropy rate serving as effective metrics for gauging sequence predictability; (2) The asymptotic equipartition property theorem indicates that the number of stratigraphic sequences increases exponentially with entropy and sequence length, underscoring the stochastic complexity inherent in stratigraphic sequences; (3) Entropy and entropy rate values allow us to quantitatively distinguish between various sedimentary environments. Additionally, the stationary probability of the Markov pure-jump process aids in quantitatively assessing differences among stratigraphic sequences within similar sedimentary contexts; (4) Quantifying the uncertainty associated with stratigraphic states and their thicknesses provides valuable geological insights, aiding geologists in making informed decisions. We also present a sensitivity analysis of our approach and outline directions for future research. The insights gained from this study underscore the potential of our methodology in enhancing the understanding of stratigraphic sequence uncertainty, facilitating more informed decision-making in related disciplines. This research paves the way for a more quantitative approach to stratigraphy.

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Section: Marine Sandstonesmentioning

confidence: 82%

Sedimentary stratigraphic uncertainty: A quantitative analysis framework based on information theory and stochastic processes

Lei,

Yan,

et al. 2024

Preprint

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“…The Donghe sandstone member is a typical marine clastic reservoir rich in oil and gas in China. 33 Limited by the “three highs” of high temperature, high salinity, and high pressure, appropriate injection and production systems need to be formulated during the development. After tracking and analyzing the oil and gas production in the process of gas injection and oil displacement, it is found that the oil and gas production of gas injection and oil displacement technology is directly related to the injection volume of gas injection wells, water injection wells, and the flowing bottom-hole pressure of production wells.…”

Section: Resultsmentioning

confidence: 99%

“…The Donghe sandstone member is a typical marine clastic reservoir rich in oil and gas in China. 33 Limited by the "three highs" of high temperature, high salinity, and high pressure, appropriate injection and production systems need to be formulated during the development.…”

Section: Resultsmentioning

confidence: 99%

“…The reservoir data used in this paper is from the Tarim oilfield, and the Donghe sandstone member in Tarim Basin is taken as the research object. The Donghe sandstone member is a typical marine clastic reservoir rich in oil and gas in China . Limited by the “three highs” of high temperature, high salinity, and high pressure, appropriate injection and production systems need to be formulated during the development.…”

Section: Resultsmentioning

confidence: 99%

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Reservoir Production Prediction Model Based on a Stacked LSTM Network and Transfer Learning

et al. 2021

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Gas injection and water injection are common and effective methods to improve oil recovery. To ensure its production effect, it is necessary to simulate the oilfield production process. However, traditional composition simulation runs a large number of calculations and takes a long time. Through the analysis of relevant data, we found that production is affected by many factors and has a strong sequential character. Therefore, this paper proposes a deep learning model for reservoir production prediction based on stacked long short-term memory network (LSTM). It is applied to other well patterns with a short production time and a few samples in the same oilfield block by transfer learning. The model achieves an effective combination with the actual reservoir production process. At the same time, it uses the knowledge learned from the well pattern with sufficient historical data to assist in the establishment of the model of the well pattern with limited data. This can obtain accurate prediction results and save the model training time, thus getting more effective application effects than composition simulation. This paper verifies the effectiveness of the proposed method through the data and multiple different injection combinations of the Tarim oilfield.

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“…As the compaction continues, the shale enters the middle diagenetic stage. The formation temperature and pressure increase, and the montmorillonite continues to dehydrate, resulting in interlayer collapse [56]. Organic matter generates hydrocarbons in large quantities and produces organic acid, resulting in acidic pore fluid [57].…”

Section: Diagenetic Evolution Sequence Of Shalementioning

confidence: 99%

Lacustrine Shale Diagenesis—A Case Study of the Second Member of the Funing Formation in the Subei Basin

Wang

Sun

et al. 2023

Processes

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Shale diagenesis differs from that of sandstone and carbonate rocks with regard to the type, evolution stage, and evolution mode. The quality of shale reservoirs is closely linked to the extent of diagenetic evolution. This study identifies the types and characteristics of shale diagenesis using thin sections and scanning electron microscopy (SEM) observations. The stages of shale diagenesis are determined by analyzing organic matter evolution and clay mineral transformation and establishing a diagenetic evolution sequence. This paper describes the comprehensive diagenetic evolution of organic matter, clay minerals, clastic particles, and carbonate minerals to determine the diagenesis types, diagenetic sequences, and pore evolution occurring during diagenetic evolution. The results show that the diagenesis types of shale in the second member of the Funing Formation include compaction, dissolution, cementation, metasomatism, dolomitization, syneresis, and transformation of clay minerals, as well as thermal evolution of organic matter. The middle diagenetic A stage is prevalent, with some areas in the early and middle diagenetic B stages. The shale underwent a diagenetic evolution sequence, including the collapse and shrinkage of montmorillonite interlayers in the early stage; the rapid formation and transformation of illite and smectite mixed layers, massive hydrocarbon generation of organic matters, and dissolution of unstable components in the middle stage; and the occurrence of fractures filled with gypsum, quartz, ferrocalcite, or other authigenic minerals in the later stage. Dissolution pores and fractures are the dominant shale reservoirs of the second member of the Funing Formation in the Subei Basin. The results provide new insights into understanding the formation and evolution of reservoir spaces during shale diagenesis and information for the exploration and development of lacustrine shale oil and gas.

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Siliciclastic depositional model within a subsurface coastal parasequence based on barriers and baffles – An application to the Donghe Sandstone Member in the Hudson Oilfield, Tarim Basin, NW China

Cited by 8 publications

References 20 publications

Sedimentary stratigraphic uncertainty: A quantitative analysis framework based on information theory and stochastic processes

Sedimentary stratigraphic uncertainty: A quantitative analysis framework based on information theory and stochastic processes

Reservoir Production Prediction Model Based on a Stacked LSTM Network and Transfer Learning

Lacustrine Shale Diagenesis—A Case Study of the Second Member of the Funing Formation in the Subei Basin

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