Pore Structure Characteristics and Permeability Prediction Model in a Cretaceous Carbonate Reservoir, North Persian Gulf Basin

Lu, Hao; Tang, Hongming; Wang, Meng; Li, Xin; Zhang, Liehui; Wang, Qiang; Zhao, Yulong; Zhao, Feng; Liao, Jijia

doi:10.1155/2021/8876679

Cited by 10 publications

(8 citation statements)

References 67 publications

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Section: Resultsmentioning

confidence: 99%

“…Kh2 is the major hydrocarbon‐producing member. Six rock types are developed in the Kh Formation, which is grainstone (RT1), algal packstone (RT2), algal wackestone (RT3), foraminiferal wackestone (RT4), mudstone (RT5) and echinoderm wackestone (RT6) (Lu et al, 2021). A large number of bioturbance structures result in strong heterogeneity of the reservoir in Kh2.…”

Section: Geological Settingmentioning

confidence: 99%

“…In combination with the characteristics of pore types, pore throat radius distribution and permeability, we qualitatively divided the PS of Kh2 into four types (Lu et al, 2021), the corresponding characteristic is shown in Figure 5. (2) Type II Throat radius distribution shows a right-skewed multi-peak pattern, indicating that the pore throat structure is complicated, both mesopore and micropore coexist.…”

Section: Pore Throat Classification From Core Datamentioning

confidence: 99%

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Prediction of the pore structure by machine learning techniques in the carbonate reservoirs in Iraq H oilfield

Zhang

et al. 2023

Geological Journal

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Pore structure impacts the capability of seepage pattern of subsurface fluid mineral and mineral exploitation efficiency. Because of the strong heterogeneity in carbonate reservoirs, the pore structure is nonlinearly varying and complex in reservoirs. It is necessary to establish a method for pore structure type (PST) prediction. Machine learning provides an efficient prediction method by finding the relationship between core test data and well‐logging data. In this paper, a reservoir identification method based on Gradient Boost Decision Trees (GBDT) is proposed for the pore structure characteristics of carbonate reservoirs integrating core test and logging data. Core testing data are utilized to form the training set for PST prediction. Then, we adopt the mutual information method to optimize the logging data as the input of the machine learning model. GBDT hyperparameters are optimized using the learning curve to make it have optimal performance. Finally, we compare the predicted result of GBDT with K‐Nearest Neighbor and support vector machines, demonstrating the superior performance of GBDT. The results indicate that the four PS are different in some properties, such as permeability, pore radius and shape of pore and throat. 20%, 10 and 0.15 are recommended as the optimal parameter values for Maximum feature, iteration number and Sub‐sample number to improve GBDT accuracy in carbonate reservoir PST prediction. Evaluation indicators of machine learning, including the Kappa coefficient, Heming distance, Jaccard similarity coefficient and Confusion matrices, indicate that GBDT has better performance. This research proves the high accuracy and applicability of machine learning technology in the carbonate reservoir and also provides a meaningful reference for the identification of the carbonate reservoirs in the central area of Iraq.

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Section: Resultsmentioning

confidence: 99%

Section: Geological Settingmentioning

confidence: 99%

Section: Pore Throat Classification From Core Datamentioning

confidence: 99%

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Prediction of the pore structure by machine learning techniques in the carbonate reservoirs in Iraq H oilfield

Zhang

et al. 2023

Geological Journal

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“…The study of shale reservoir pore types is the basis of shale reservoir evaluation. , The pore size of shale is small, and the pore morphology and genesis are diverse. The micro–nano connected pores and micro-fractures together form a pore network for gas occurrence and seepage. − The mountain shales in the study area mainly develop intergranular pores and organic pores. The organic pores of the high-carbon siliceous shales are very well developed, with a predominantly rounded and subrounded morphology (Figure a,b), pore size usually greater than 100 nm, good connectivity, and an average porosity of 5.0% (Table ).…”

Section: Resultsmentioning

confidence: 99%

Lithofacies Types and Reservoir Characteristics of Mountain Shale in Wufeng Formation-Member 1 of Longmaxi Formation in the Complex Structural Area of Northern Yunnan–Guizhou

Chai

Zhao

Ji³

et al. 2023

ACS Omega

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The mountain shale in Wufeng Formation-Member 1 of Longmaxi Formation in the complex structural area of northern Yunnan−Guizhou has great potential for exploration and development. In order to clarify the differences of reservoir quality and the longitudinal distribution law of different lithofacies, the lithofacies in Wufeng Formation-Member 1 of Longmaxi Formation was divided combined with core, logging, and analytical test data. Based on the data of total organic carbon, laminate structure, reservoir porosity types and physical properties, and gas content, the reservoir characteristics and advantageous lithofacies shale reservoir distribution were determined. The results show that the mountain shale lithofacies in the study area are divided into 7 major types and 20 subtypes. The high-carbon siliceous shale has the highest degree of organic pore development, specific surface area (average 28.55 cm 2 /g), and pore volume (average 0.0397 cm 3 / g). Two types of advantageous lithofacies shale reservoir in the study area were identified. The high-carbon siliceous shale reservoir could be considered as an excellent shale reservoir for shale gas and is mainly concentrated in the first section. It is mainly developed in layer-1 and the bottom of layer-2 of the Longmaxi Formation, providing favorable source-reservoir conditions for shale gas enrichment. The medium-carbon siliceous shale, medium-carbon clay siliceous shale, and medium-carbon calcareous siliceous shale, developed in the top of layer-2 and the bottom of layer-3 of the Longmaxi Formation, are assumed to be moderately promising for shale gas. Therefore, the research results deepen the understanding of the longitudinal distribution of the dominant shale reservoirs in the study area and are of great significance in promoting strategic transfer of the main shale gas exploration system in the south from the Sichuan basin to the outer basin.

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“…Figure 4 shows the effect of residual water saturation on the formation factor at standard and reservoir conditions. The formation factor generally indicates by how many times the resistivity of a partially water-saturated rock increases compared to its resistivity when the pore volume is fully saturated with water [28,29]. Moreover, since the volume of water in the pores of the rock samples does not change during the transition from the standard conditions to the reservoir conditions, the dependences of the formation factor on water saturation turn out to be close in this case.…”

Section: Figurementioning

confidence: 93%

Experimental Study About Water Saturation Influence on Changes in Reservoirs Petrophysical Properties

Al-Obaidi

Smirnov

Alwan³

2021

Walailak J Sci & Tech

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Experimental determination of the physical properties of rocks under conditions simulating in situ reservoir conditions is of great importance both for the calculation of reserves and for the interpretation of well logging data. In addition, it is also important for the preparation of hydrocarbon field development projects. The study of the processes of changes in the petrophysical properties of the reservoir under controlled conditions allows not only to determine their reliability but also to evaluate the dynamics of these changes depending on the temperature and pressure conditions of the reservoir and the water saturation of the rocks. In this work, an evaluation of the dependence of the physical properties of hydrocarbon reservoirs on their water saturation (Sw) was carried out. Residual water saturation (Swr) was created in the rocks and the properties of these rocks were compared at the states of partial (25 %) and complete water saturation (100 %). The changes in petrophysical parameters of partially water saturated rocks during the increase in effective pressure were studied and estimates of these changes were obtained. The results showed that when the effective pressure is increased, the Swr increases by an average of 6 % compared to atmospheric conditions. This is accompanied by an increase in the velocity of longitudinal (by 51.9 % on average) and lateral waves (by 37.1 % on average). As residual water saturation increases, effective permeability decreases for both standard and reservoir conditions, with, gas permeability decreasing for both dry samples (by 23 % on average) and samples with residual water saturation (effective permeability decreases by 27 % on average). HIGHLIGHTS Changes in physical properties of hydrocarbon reservoirs by determining physical properties (permeability, porosity, elastic, electrical, deformation strength) under the standard conditions and in physical modelling of reservoir conditions and processes Assessment of the effectiveness of water saturation on the physical properties of the reservoir Comparisons between the petrophysical properties of reservoir core samples in which the pore space is fully saturated with the reservoir fluid model and samples with residual water saturation Experimental determination of the physical properties of rocks under conditions simulating in situ reservoir conditions Estimation of the changes in petrophysical parameters of partial water-saturated rocks during the increase in effective formation pressure GRAPHICAL ABSTRACT

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Pore Structure Characteristics and Permeability Prediction Model in a Cretaceous Carbonate Reservoir, North Persian Gulf Basin

Cited by 10 publications

References 67 publications

Prediction of the pore structure by machine learning techniques in the carbonate reservoirs in Iraq H oilfield

Prediction of the pore structure by machine learning techniques in the carbonate reservoirs in Iraq H oilfield

Lithofacies Types and Reservoir Characteristics of Mountain Shale in Wufeng Formation-Member 1 of Longmaxi Formation in the Complex Structural Area of Northern Yunnan–Guizhou

Experimental Study About Water Saturation Influence on Changes in Reservoirs Petrophysical Properties

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