Coal-bed methane reservoir characterization using well-log data

2022 ◽  
pp. 243-274
Author(s):  
David A. Wood ◽  
Jianchao Cai
Keyword(s):  
Reservoir Characterization ◽  
Coal Bed ◽  
Coal Bed Methane ◽  
Well Log ◽  
Log Data

Brittleness index calculation based on amplitude-variation-with-offset inversion for coal-bed methane reservoir: A case study of the Qinshui Basin, China

2020 ◽  
Vol 8 (1) ◽  
pp. SA63-SA72
Author(s):  
Wu Haibo ◽  
Cheng Yan ◽  
Zhang Pingsong ◽  
Dong Shouhua ◽  
Huang Yaping
Keyword(s):  
Seismic Data ◽  
Brittleness Index ◽  
Coal Bed ◽  
Coal Bed Methane ◽  
Well Log ◽  
Amplitude Variation ◽  
Frequency Model ◽  
Log Data ◽  
Amplitude Variation With Offset ◽  
Avo Inversion

The brittleness index (BI) is an important parameter for coal-bed methane (CBM) reservoir fracturing characterization. Most published studies have relied on petrophysical and well-log data to estimate the geomechanical properties of reservoir rocks. The major drawback of such methods is the lack of control away from well locations. Therefore, we have developed a method of combining BI calculation from well logs with that inverted from 3D seismic data to overcome the limitation. A real example is given here to indicate the workflow. A traditional amplitude-variation-with-offset (AVO) inversion was conducted first. BI for the CBM reservoir was then calculated from the Lamé constants inverted from prestack seismic data through a traditional AVO inversion method. We build an initial low-frequency model based on the well-log data. Comparison of the seismic inverted BI at the target reservoir and BI extracted from the well-log data showed satisfactory results. This method has been proved to be efficient and effective enough at identifying BI sweet spots in CBM reservoirs.

Managing Uncertainty of Well Log Data in Reservoir Characterization

2009 ◽  
Author(s):  
Paddy Deo ◽  
Dong Cynthia Xue ◽  
Freddy Mendez
Keyword(s):  
Reservoir Characterization ◽  
Well Log ◽  
Log Data

scholarly journals Hydrocarbon reservoir characterization of “Otan-Ile” field, Niger Delta

2021 ◽  
Vol 11 (2) ◽  
pp. 601-615
Author(s):  
Tokunbo Sanmi Fagbemigun ◽  
Michael Ayu Ayuk ◽  
Olufemi Enitan Oyanameh ◽  
Opeyemi Joshua Akinrinade ◽  
Joel Olayide Amosun ◽  
...  
Keyword(s):  
Niger Delta ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Structural Deformation ◽  
Well Log ◽  
Reservoir Properties ◽  
Good Tool ◽  
Log Data ◽  
Hydrocarbon Reservoir ◽  
Hydrocarbon Saturation

AbstractOtan-Ile field, located in the transition zone Niger Delta, is characterized by complex structural deformation and faulting which lead to high uncertainties of reservoir properties. These high uncertainties greatly affect the exploration and development of the Otan-Ile field, and thus require proper characterization. Reservoir characterization requires integration of different data such as seismic and well log data, which are used to develop proper reservoir model. Therefore, the objective of this study is to characterize the reservoir sand bodies across the Otan-Ile field and to evaluate the petrophysical parameters using 3-dimension seismic and well log data from four wells. Reservoir sands were delineated using combination of resistivity and gamma ray logs. The estimation of reservoir properties, such as gross thickness, net thickness, volume of shale, porosity, water saturation and hydrocarbon saturation, were done using standard equations. Two horizons (T and U) as well as major and minor faults were mapped across the ‘Otan-Ile’ field. The results show that the average net thickness, volume of shale, porosity, hydrocarbon saturation and permeability across the field are 28.19 m, 15%, 37%, 71% and 26,740.24 md respectively. Two major faults (F1 and F5) dipping in northeastern and northwestern direction were identified. The horizons were characterized by structural closures which can accommodate hydrocarbon were identified. Amplitude maps superimposed on depth-structure map also validate the hydrocarbon potential of the closures on it. This study shows that the integration of 3D seismic and well log data with seismic attribute is a good tool for proper hydrocarbon reservoir characterization.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Simultaneous History-Matching Approach by Use of Reservoir-Characterization and Reservoir-Simulation Studies

2016 ◽  
Vol 19 (04) ◽  
pp. 694-712 ◽  
Author(s):  
Guilherme Daniel Avansi ◽  
Célio Maschio ◽  
Denis José Schiozer
Keyword(s):  
Numerical Simulation ◽  
History Matching ◽  
Reservoir Characterization ◽  
Well Log ◽  
Log Data ◽  
Field Development ◽  
Geostatistical Methods ◽  
Well Production ◽  
Matching Process ◽  
Geological Models

Summary Reservoir characterization is the key to success in history matching and production forecasting. Thus, numerical simulation becomes a powerful tool to achieve a reliable model by quantifying the effect of uncertainties in field development and management planning, calibrating a model with history data, and forecasting field production. History matching is integrated into several areas, such as geology (geological characterization and petrophysical attributes), geophysics (4D-seismic data), statistical approaches (Bayesian theory and Markov field), and computer science (evolutionary algorithms). Although most integrated-history-matching studies use a unique objective function (OF), this is not enough. History matching by simultaneous calibrations of different OFs is necessary because all OFs must be within the acceptance range as well as maintain the consistency of generated geological models during reservoir characterization. The main goal of this work is to integrate history matching and reservoir characterization, applying a simultaneous calibration of different OFs in a history-matching procedure, and keeping the geological consistency in an adjustment approach to reliably forecast production. We also integrate virtual wells and geostatistical methods into the reservoir characterization to ensure realistic geomodels, avoiding the geological discontinuities, to match the reservoir numerical model. The proposed methodology comprises a geostatistical method to model the spatial reservoir-property distribution on the basis of the well-log data; numerical simulation; and adjusting conditional realizations (models) on the basis of geological modeling (variogram model, vertical-proportion curve, and regularized well-log data). In addition, reservoir uncertainties are included, simultaneously adjusting different OFs to evaluate the history-matching process and virtual wells to perturb geological continuities. This methodology effectively preserves the consistency of geological models during the history-matching process. We also simultaneously combine different OFs to calibrate and validate the models with well-production data. Reliable numerical and geological models are used in forecasting production under uncertainties to validate the integrated procedure.

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