Geological characteristics and abnormal pore pressure prediction in shale oil formations of the Dongying depression, China

Zhang, Yushuai; Lv, Dawei; Yong, Wang; Liu, Haiyan; Song, Guangzeng; Gao, Jian

doi:10.1002/ese3.641

Cited by 15 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the evaluation process of shale oil production capacity, understanding the extent of influence and interrelationships among various factors is crucial [16]. However, within these factors, inevitable correlations or overlaps among certain parameters may affect the final evaluation outcomes.…”

Section: Corresponding Loss Function Y Leftmentioning

confidence: 99%

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Qian,

Lu,

Zhong

et al. 2024

ENERGY

View full text Add to dashboard Cite

The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics, engineering quality, and well conditions. These relationships, nonlinear in nature, pose challenges for accurate description through physical models. While field data provides insights into real-world effects, its limited volume and quality restrict its utility. Complementing this, numerical simulation models offer effective support. To harness the strengths of both data-driven and model-driven approaches, this study established a shale oil production capacity prediction model based on a machine learning combination model. Leveraging fracturing development data from 236 wells in the field, a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs. Through the combination model integrating support vector machine (SVM) algorithm and back propagation neural network (BPNN), a model-driven shale oil production capacity prediction model is developed, capable of swiftly responding to shale oil development performance under varying geological, fluid, and well conditions. The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R 2 by 22.5% and 5.8% compared to singular machine learning models like SVM and BPNN, showcasing its superior precision in predicting shale oil production capacity across diverse datasets.

show abstract

Section: Corresponding Loss Function Y Leftmentioning

confidence: 99%

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Qian,

Lu,

Zhong

et al. 2024

ENERGY

View full text Add to dashboard Cite

show abstract

“…Previous studies have suggested that the overpressure generated by different mechanisms should be predicted in different methods [24]. The overpressure in the Dongying Depression is mainly caused by oil generation from the source rocks [1,25], while compaction disequilibrium may have contributed to the overpressure in the north Steep Slope zone of the Dongying Depression [26,27]. Thus, the pore pressure can be calculated by the Eaton Formula [28,29]:…”

Section: Excess Pressure Prediction Using Well Log Datamentioning

confidence: 99%

Quantitative Characterization of Excess Pressure Gradient in the Upper Interval of Es4 Member of Dongying Depression and Its Indicative Significance for Oil Migration and Accumulation

Zhao

Wang

et al. 2022

Energies

View full text Add to dashboard Cite

Excess pressure is the main driving force of oil migration in the source-reservoir system of overpressured petroliferous basins. It can reflect the change in driving force for oil migration and its influence on oil accumulation in overpressure transport layers. The drilling stem test (DST) data, well logging data, and seismic velocity data are used to describe the plane distribution of the excess pressures in the Es4s member of the Dongying Depression. Then, the values and directions of the excess pressure gradient, which can indicate oil migration and accumulation, are calculated based on the plane distribution of the excess pressure in the Es4s member of the Dongying Depression. The results suggest that overpressure is widely developed in the Es4s member of the Dongying Depression, and the excess pressure gradually decreases from the center to the edge of each sag, while the variation in the excess pressure gradient is characterized by “low-high-low” in a circular band around the sags. The excess pressure in the sag areas exceeds 15 MPa, but the excess pressure gradient is mainly between 0 and 1 MPa/km. The excess pressure in the northern steep slope zone of the Lijin sag and the northern steep slope zone of the Minfeng sag are less than 15 MPa, while the excess pressure gradient ranges from 1 to 7 MPa/km. The excess pressure in the central anticline belt and the gentle slope belt in the south of the Niuzhuang sag are between 0 and 15 MPa, and the excess pressure gradient is from 0 MPa/km to 2 MPa/km. From geochemical evidence, local oil migration directions indicated by the excess pressure gradient are consistent with those indicated by the ratio parameters of carbazole compounds in crude oil samples, indicating that the direction of the excess pressure gradient can indicate the dominant direction of oil migration driven by excess pressure, and the oil from the Es4s source rock is mainly distributed in the areas with a high excess pressure gradient or the areas with a low excess pressure gradient and low excess pressure (area II).

show abstract

“…Reservoir characteristics of the sandstone such as geometry, mineralogical composition, pore characteristics, porosity, permeability, textural framework, and maturity are evaluated by depositional settings of the sandstones, whereas the diagenesis may affect the reservoir porosity by post-depositional processes (Bjørlykke 2014;Cantarero et al 2020;Mangi et al 2020). Reservoir quality is the main factor controlling the prospectivity during the exploration and production of hydrocarbon (Zhang et al 2020). Lower Goru sandstone reservoirs in the Lower Indus basin have been studied with the help of core cuttings of well KD-01 well from the sandstone intervals.…”

Section: Introductionmentioning

confidence: 99%

The impact of diagenesis on the reservoir quality of the early Cretaceous Lower Goru sandstones in the Lower Indus Basin, Pakistan

Dar

Baiyegunhi

et al. 2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

The sandstone units of the Early Cretaceous Lower Goru Formation are significant reservoir for gas, oil, and condensates in the Lower Indus Basin of Pakistan. Even though these sandstones are significant reservoir rocks for hydrocarbon exploration, the diagenetic controls on the reservoir properties of the sandstones are poorly documented. For effective exploration, production, and appraisal of a promising reservoir, the diagenesis and reservoir properties must be comprehensively analyzed first. For this study, core samples from depths of more than 3100 m from the KD-01 well within the central division of the basin have been studied. These sandstones were analyzed using petrographic, X-ray diffraction, and scanning electron microscopic analyses to unravel diagenetic impacts on reservoir properties of the sandstone. Medium to coarse-grained and well-sorted sandstone have been identified during petrographic study. The sandstone are categorized as arkose and lithic arkose. Principal diagenetic events which have resulted in changing the primary characters of the sandstones are compaction, cementation, dissolution, and mineral replacement. The observed diagenetic processes can be grouped into early, burial, and late diagenesis. Chlorite is the dominant diagenetic constituent that occurs as rims, coatings, and replacing grains. The early phase of coating of authigenic chlorite has preserved the primary porosity. The recrystallization of chlorite into chamosite has massively reduced the original pore space because of its bridging structure. The current study reveals that diagenetic processes have altered the original rock properties and reservoir characteristics of the Lower Goru sandstone. These preliminary outcomes of this study have great potential to improve the understanding of diagenetic process and their impact on reservoir properties of the Lower Goru sandstone in the Lower Indus Basin and adjoining areas.

show abstract

Geological characteristics and abnormal pore pressure prediction in shale oil formations of the Dongying depression, China

Cited by 15 publications

References 29 publications

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Quantitative Characterization of Excess Pressure Gradient in the Upper Interval of Es4 Member of Dongying Depression and Its Indicative Significance for Oil Migration and Accumulation

The impact of diagenesis on the reservoir quality of the early Cretaceous Lower Goru sandstones in the Lower Indus Basin, Pakistan

Contact Info

Product

Resources

About