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Due to the diversity of pore types, it is challenging to characterize the Middle East's Cretaceous carbonate reservoir or accurately predict its petrophysical properties. In this paper, pore structure in the reservoir is first classified using a comprehensive method. Then, based on the identified pore structure types, a new permeability model with high prediction precision is established. The reservoir is dominated by 6 pore types, such as intergrain pores and moldic pores, and 6 rock types. Grainstone, algal packstone, algal wackestone, and foraminifera wackestone are porous rock types, and echinoderm wackestone and mudstone are nonporous rock types. The types of pore structure in the study area can be divided into four types. Type I has midhigh porosity and medium-high permeability due to its large throat, while type II has a fine throat type with midhigh porosity and midpermeability. Due to their isolated pores, the permeability is low in types III and IV, and out of these two, type III has better storage capacity. Movable fluid saturation calculated by the spectral coefficient method and r apex can characterize the boundary between the connected pores and unconnected pores very well in the research area. It is not accurate enough to simply classify the pore structure by permeability and porosity. The combination of porosity, permeability, r apex , flow zone indicator, and the reservoir quality index can effectively distinguish and classify pore structure types in noncoring wells. The characteristics of each pore structure type are consistent with those of the fractal dimension, which thereby proves the effectiveness of the pore structure classification. New permeability prediction models are proposed for different pore structure types, and good prediction results have been obtained. This study is of great significance for enhancing oil recovery. [ABSTRACT FROM AUTHOR]

The Yingshan Formation in Lower Ordovician is an important hydrocarbon reservoir of the Tarim Basin, northwest China; however, the factors controlling reservoir development in the formation are not fully understood. Data from thin-section observations, scanning electron microscopy, and gamma-ray logging were applied to produce a fourth-order sequence stratigraphic framework for the Yingshan Formation in the Yubei area of the Tarim Basin. The framework was assessed in terms of the carbonate diagenesis of the reservoir rock. The influence of diagenesis on reservoir porosity, the factors restricting reservoir quality, and the significance of the results for exploration are discussed. Diagenesis, dolomitization, and cementation by atmospheric fresh water occurred in the lowstand systems tracts. The sediments in the transgressive systems tract were consolidated by seawater, or dolomitization occurred in the landward mixed zone, or the sediments quickly entered the shallow burial marine diagenetic environment and became compacted. The highstand systems tract experienced significant diagenesis, with seafloor cementation, atmospheric water dissolution and dolomitization. These factors were related to sea-level fluctuation and sedimentary features. The porosity of some high-frequency sequences in the lower part of the Yingshan Formation was improved significantly. In the Yubei area, cementation of the middle part of the fourth-order sequence during burial reduced the porosity and permeability of the reservoir. This was the main cause of reservoir deterioration. During oil and gas exploration, attention should be paid to the higher degree of dissolution in the middle part of the fourth-order sequence and the superposition of atmospheric fresh-water dissolution in the upper part of the sequence. • Gamma-ray logging (Th/K ratio) were applied to produce a high-frequency sequence stratigraphic framework. • In Yingshan Formation, burial dolomitization, seafloor cementation and burial dissolution occurs in TST, seafloor cementation and burial dissolution occurs in HST, atmospheric fresh-water dissolution and atmospheric fresh-water cementation occurs in LST. • The superposition of the burial dissolution and the atmospheric fresh-water dissolution is the key to the formation of the high-quality reservoir in the middle of the Yingshan Formation. [ABSTRACT FROM AUTHOR]

Lu, H.; Tang, H.M.; Wang, M., and Wang, Y.J., 2020. Characterization of a carbonate reservoir formed in a low energy environment. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 775–784. Coconut Creek (Florida), ISSN 0749-0208. The Cretaceous oil and gas reservoir located in the Middle East is one of the most important reservoirs in the world. However, it is highly inhomogeneous and porous and displays low permeability. These characteristics affect is potential as high quality reservoir. The present research integrated conventional petrophysical tests, high-pressure mercury intrusion analysis, scanning electron microscopy (SEM) and petrological analysis to determine the rock characteristics of the Kh2 upper cretaceous oil field. With this information we classified the reservoir in different types. The results showed that the reservoir was formed in a low energy environment, is an open platform running from above the wave base level to below the low tide line. The lithology was dominated by foraminiferal biomicritie, echinate biomicritie, micritie, pelsparite, algae biopelmicrite, algal biomicritie, and algal fossiliferous micrite. Based on the regional structure and palaeogeodetic sedimentary background, the presence of open sea (MF1), high-energy pellets shoal (MF2), intershoal sea (MF3), low-energy pellets shoal (MF4), and low-energy bioclastic shoal (MF5) were identified. The porosity ranged from 10% to 29%, with an average of 23.73%. The permeability varied between 0.4 mD and 490 mD, with an average value of 21.21 mD. In addition, the petrophysical properties were controlled by sedimentary microfacies and diagenesis, and the quality of these properties decreased in the order MF2, MF4, MF5, MF3, MF1. Diagenesis transformed the reservoir; however, the basis for diagenesis was the sedimentary environment. The reservoir was divided into 3 types, which corresponded to 3 types of pore structures. The first type (pore throat structure I) mainly developed in MF2 and MF4, where the average throat radius was larger than 2.2 µm, and the permeability higher than 20 mD. The second type (pore throat structure II) was identified in MF4 and MF5, presenting an average throat radius between 0.5 µm and 2.2 µm, and a permeability between 5 mD and 20 mD. The third type (pore throat structure III) occurred in MF1 and MF3, presented average throat radius smaller than 0.5 µm, and permeability smaller than 5 mD. The Kh2 layer was a type II reservoir. The results presented herein will be of great significance for the identification of potential oil research and development areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pores, fractures, caves, and other storage spaces are commonly distributed in fractured-vuggy carbonate reservoirs. During the drilling process, more than half of all drill-in fluid loss issues are caused by developed caves. Cave scales range from centimeters to meters, making leak prevention increasingly difficult through the use of traditional technologies. Currently, there is still high demand for the understanding of feasible loss control techniques, especially in fractured-vuggy carbonate reservoirs. Multistage Bridge Plugging (MBP) technology has facilitated pioneering experiments in many oilfields, but the success rate of plugging is less than 50%, and the effects of plugging are uncontrollable and difficult to predict. This is due to a lack of clarity regarding the plugging mechanism and the key controlling factors. In this study, we used the Discrete Element Method (DEM) simulation method to investigate the controlling factors of MBP technology, and analyzed its applicable conditions. We found that the prerequisite for the success of MBP is the presence of a constricted throat near the wellbore when drilling the well hole; the first-stage bridging ball is the key to the success of MBP. Larger ball radius, cave inclination and initial flow rate, and lower ball velocity are beneficial to the first-stage bridging. All discussion in this research is based on the ideal situation. However, the cave pattern is difficult to describe using several models, let alone by one ideal model. With the progress of seismic fine description technology and mud logging, more accurate characterization of caves in carbonate reservoirs will help to accurately formulate the plugging scheme and greatly improve the success rate of plugging technology. Additionally, the engineering risks of this technology, such as plugging the coiled tubing, need to be further studied. [ABSTRACT FROM AUTHOR]