Application of the Multi-Parameter Geostatistical Inversion in the Carbonate Reservoir Prediction

3D Multi-Level Facies Constrained Geostatistical Inversion for Predicting Carbonate Thin Reservoirs

10.2118/207344-ms ◽

2021 ◽

Author(s):

Tongcui Guo ◽

Lirong Dou ◽

Guihai Wang ◽

Dongbo He ◽

Hongjun Wang ◽

...

Keyword(s):

Prediction Method ◽

Rock Physics ◽

Simulation Method ◽

Horizontal Resolution ◽

Carbonate Reservoir ◽

Carbonate Reservoirs ◽

Reservoir Prediction ◽

Geostatistical Inversion ◽

Water Layers ◽

Multi Level

Abstract Carbonate reservoirs are highly heterogeneous and poor in interwell connectivity. Therefore, it is difficult to predict the thin oil layers and water layers inside the carbonate reservoir with thickness less than 10 ft by seismic data. Based on the petrophysical analysis with core and well logging data, the carbonate target layers can be divided into two first level lithofacies (reservoir and non-reservoir) and three second-level lithofacies (oil, water and non-reservoir) by fluids. In this study, the 3D lithofacies probabilistic cubes of the first level and second-level level lithofacies were constructed by using the simulation method of well-seismic cooperative waveform indication. Afterwards, constrained by these probability cubes, the prestack geostatistical inversion was carried out to predict the spatial distribution of thin oil layers and water layers inside the thin reservoir. The major steps include: (1) Conduct rock physics analysis and lithofacies classification on carbonate reservoirs; (2) Construct the models constrained by two-level lithofacies; (3) Predict thin reservoirs in carbonates by prestack geostatistical inversion under the constraint of two-level lithofacies probability cubes. The prediction results show that through the two-level lithofacies-controlled prestack geostatistical inversion, the vertical and horizontal resolution of thin oil layers and water layers in carbonate reservoirs has been improved significantly, and the accuracy of thin oil reservoir prediction and the analyzing results of interwell oil layer connectivity have been improved significantly. Compared with the actual drilling results, the prediction results by 3D multi-level lithofacies-controlled inversion are consistent with the drilling results, and the details of thin carbonate reservoirs can be predicted. It has been proved that this method is reasonable and feasible. With this method, the prediction accuracy on thin reservoirs can be improved greatly. Compared with the conventional geostatistical inversion results, the 3D multi-level lithofacies-controlled inversion can improve significantly the vertical and horizontal resolution of prediction results of thin reservoirs and thin oil layers, and improve the reliability of interwell prediction results. For the prediction of thin carbonate reservoirs with serious heterogeneity, the 3D multi-level lithofacies-controlled inversion is an effective prediction method.

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Geostatistical Inversion for Accurate Carbonate Reservoir Prediction - A Case Study in Leijia Area of Liaohe Oilfield

77th EAGE Conference and Exhibition 2015 ◽

10.3997/2214-4609.201413077 ◽

2015 ◽

Author(s):

Z.T. Liu ◽

S.Z. Sun ◽

X.G. Li ◽

Y.Y. Sun ◽

P.F. Wang ◽

...

Keyword(s):

Carbonate Reservoir ◽

Reservoir Prediction ◽

Liaohe Oilfield ◽

Geostatistical Inversion

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Application of the Multi-Parameter Geostatistical Inversion in the Carbonate Reservoir Prediction

10.2523/iptc-17141-ms ◽

2013 ◽

Author(s):

Cai Qin Liu ◽

Zhao Feng Wang ◽

Xiao Dong Wei ◽

Da Wei Zhang ◽

Bo Peng ◽

...

Keyword(s):

Carbonate Reservoir ◽

Reservoir Prediction ◽

Geostatistical Inversion

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3D Seismic attribute optimization technology and application for dissolution caved carbonate reservoir prediction

10.1190/1.3627592 ◽

2011 ◽

Author(s):

Lifeng Liu ◽

Sam Zandong Sun ◽

Haiyang Wang ◽

Haijun Yang ◽

Jianfa Han ◽

...

Keyword(s):

Carbonate Reservoir ◽

3D Seismic ◽

Reservoir Prediction ◽

Seismic Attribute

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Deep carbonate reservoir characterisation using multi-seismic attributes via machine learning with physical constraints

Journal of Geophysics and Engineering ◽

10.1093/jge/gxab049 ◽

2021 ◽

Vol 18 (5) ◽

pp. 761-775

Author(s):

Yuanyuan Chen ◽

Luanxiao Zhao ◽

Jianguo Pan ◽

Chuang Li ◽

Minghui Xu ◽

...

Keyword(s):

Machine Learning ◽

Domain Knowledge ◽

Model Building ◽

Seismic Attributes ◽

Carbonate Reservoir ◽

Western China ◽

Reservoir Quality ◽

Reservoir Prediction ◽

Physical Constraints ◽

Highly Nonlinear

Abstract Seismic characterisation of deep carbonate reservoirs is of considerable interest for reservoir distribution prediction, reservoir quality evaluation and reservoir structure delineation. However, it is challenging to use the traditional methodology to predict a deep-buried carbonate reservoir because of the highly nonlinear mapping relationship between heterogeneous reservoir features and seismic responses. We propose a machine-learning-based method (random forest) with physical constraints to enhance deep carbonate reservoir prediction performance from multi-seismic attributes. We demonstrate the effectiveness of this method on a real data application in the deep carbonate reservoir of Tarim Basin, Western China. We first perform feature selection on multi-seismic attributes, then four kinds of physical constraint (continuity, boundary, spatial and category constraint) transferred from domain knowledge are imposed on the process of model building. Using the physical constraints, the F1 score of reservoir quality and reservoir type can be significantly improved and the combination of the effective physical constraints gives the best prediction of performance. We also apply the proposed strategy on 2D seismic data to predict the spatial distribution of reservoir quality and type. The seismic prediction results provide a reasonable description of the strong heterogeneity of the reservoir, offering insights into sweet spot detection and reservoir development.

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Prediction of hydrocarbon reservoirs within coal-bearing formations

Journal of Geophysics and Engineering ◽

10.1093/jge/gxaa007 ◽

2020 ◽

Vol 17 (3) ◽

pp. 484-492 ◽

Cited By ~ 2

Author(s):

Faqi He ◽

Ying Rao ◽

Weihong Wang ◽

Yanghua Wang

Keyword(s):

Gamma Ray ◽

Ordos Basin ◽

Low Frequency ◽

Careful Analysis ◽

Hydrocarbon Reservoirs ◽

Seismic Profiles ◽

Tight Sandstone ◽

Reservoir Prediction ◽

Gas Field ◽

Geostatistical Inversion

Abstract This paper presents a case study on the prediction of hydrocarbon reservoirs within coal-bearing formations of the Upper Palaeozoic. The target reservoirs are low-permeability low-pressure tight-sandstone reservoirs in the Daniudi Gas Field, Ordos Basin, China. The prime difficulty in reservoir prediction is caused by the interbedding coal seams within the formations, which generate low-frequency strong-amplitude reflections in seismic profiles. To tackle this difficulty, first, we undertook a careful analysis regarding the stratigraphy and lithology of these coal-bearing formations within the study area. Then, we conducted a geostatistical inversion using 3D seismic data and obtained reservoir parameters including seismic impedance, gamma ray, porosity and density. Finally, we carried out a reservoir prediction in the coal-bearing formations, based on the reservoir parameters obtained from geostatistical inversion and combined with petrophysical analysis results. The prediction result is accurately matched with the actual gas-test data for the targeted four segments of the coal-bearing formations.

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