Well-log inversion and modeling—A tool for understanding ambiguity and sensitivity of seismic data to changes in petrophysical properties

2007 ◽  
Vol 26 (7) ◽  
pp. 812-817 ◽  
Author(s):  
Alvaro Chaveste ◽  
Fred Hilterman
Keyword(s):  
Seismic Data ◽  
Well Log ◽  
Petrophysical Properties

scholarly journals Forced Fold Amplitude and Sill Thickness Constrained by Wireline and 3-D Seismic Data Suggest an Elastic Magma-Induced Deformation in Tarim Basin, NW China

Minerals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 293
Author(s):  
Wei Tian ◽  
Xiaomin Li ◽  
Lei Wang
Keyword(s):  
Tarim Basin ◽  
Seismic Data ◽  
Inelastic Deformation ◽  
Northwest China ◽  
Well Log ◽  
Total Thickness ◽  
Nw China ◽  
Seismic Method ◽  
Two Parameters ◽  
Outer Area

Disparities between fold amplitude (A) and intrusion thickness (Hsill) are critical in identifying elastic or inelastic deformation in a forced fold. However, accurate measurements of these two parameters are challenging because of the limit in separability and detectability of the seismic data. We combined wireline data and 3-D seismic data from the TZ-47 exploring area in the Tarim Basin, Northwest China, to accurately constrain the fold amplitude and total thickness of sills that induced roof uplift in the terrain. Results from the measurement show that the forced fold amplitude is 155.0 m. After decompaction, the original forced fold amplitude in the area penetrated by the well T47 ranged from 159.9 to 225.8 m, which overlaps the total thickness of the stack of sills recovered by seismic method (171.4 m) and well log method (181.0 m). Therefore, the fold amplitude at T47 area is likely to be elastic. In contrast, the outer area of the TZ-47 forced fold is characterized by shear-style deformation, indicating inelastic deformation at the marginal area. It is suggested that interbedded limestone layers would play an important role in strengthening the roof layers, preventing inelastic deformation during the emplacement of intrusive magma.

Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair
Keyword(s):  
Neural Network ◽  
Bulk Density ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Petrophysical Properties ◽  
Seismic Section ◽  
Frequency Model ◽  
Seismic Reflection Data ◽  
Reflection Data

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

Stochastic discrete fracture network modeling in shale reservoirs via integration of seismic attributes and petrophysical data

2021 ◽  
pp. 1-50
Author(s):  
Yongchae Cho
Keyword(s):  
Seismic Data ◽  
Stochastic Approach ◽  
Fracture Network ◽  
Natural Fracture ◽  
Well Logs ◽  
Fracture Model ◽  
Well Log ◽  
Discrete Fracture ◽  
Discrete Fracture Network Modeling ◽  
Fracture Network Modeling

The prediction of natural fracture networks and their geomechanical properties remains a challenge for unconventional reservoir characterization. Since natural fractures are highly heterogeneous and sub-seismic scale, integrating petrophysical data (i.e., cores, well logs) with seismic data is important for building a reliable natural fracture model. Therefore, I introduce an integrated and stochastic approach for discrete fracture network modeling with field data demonstration. In the proposed method, I first perform a seismic attribute analysis to highlight the discontinuity in the seismic data. Then, I extrapolate the well log data which includes localized but high-confidence information. By using the fracture intensity model including both seismic and well logs, I build the final natural fracture model which can be used as a background model for the subsequent geomechanical analysis such as simulation of hydraulic fractures propagation. As a result, the proposed workflow combining multiscale data in a stochastic approach constructs a reliable natural fracture model. I validate the constructed fracture distribution by its good agreement with the well log data.

Global optimization to retrieve borehole-derived petrophysical properties of carbonates

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. D75-D82
Author(s):  
Alireza Shahin ◽  
Mike Myers ◽  
Lori Hathon
Keyword(s):  
Dielectric Constant ◽  
Electrical Resistivity ◽  
Global Optimization ◽  
Joint Inversion ◽  
Joint Modeling ◽  
Carbonate Reservoir ◽  
Dual Porosity ◽  
Model Parameters ◽  
Well Log ◽  
Petrophysical Properties

Joint modeling and inversion of frequency-dependent dielectric constant and electrical resistivity well-log measurements has been addressed in literature in recent years. However, this problem is not studied for dual-porosity carbonate formations. Besides, the salinity and matrix dielectric constant are presumed to be known in previous studies. We have combined a model for brine dielectric constant and two laboratory-supported models for the electrical resistivity and dielectric constant of dual-porosity carbonates. Using this methodology, we replicate electrical resistivity and dielectric well-log measurements. Using a stochastic global optimization algorithm, we formulate a joint inversion workflow to estimate petrophysical properties of interest. For a constructed dual-porosity carbonate reservoir, we determine that the inversion workflow matches the forward-modeled data for the oil column, water column, and transition zone. We also found that our inversion workflow is capable to retrieve local model parameters (water-filled intergranular porosity and water-filled vuggy porosity) and global model parameters (matrix dielectric constant, lithology exponents for intergranular and vuggy pores, and salinity) with reasonable accuracy.

THE INTEGRATED USE OF PETROPHYSICAL, AVO, SEISMIC INVERSION AND 3D VISUALISATION TOOLS FOR RESERVOIR CHARACTERISATION

2001 ◽  
Vol 41 (2) ◽  
pp. 131
Author(s):  
A.G. Sena ◽  
T.M. Smith
Keyword(s):  
Seismic Data ◽  
New Technology ◽  
Seismic Inversion ◽  
Well Log ◽  
Reservoir Properties ◽  
Gas Field ◽  
Geological Interpretation ◽  
3D Visualisation ◽  
Quantitative Understanding ◽  
Inversion Techniques

The successful exploration for new reservoirs in mature areas, as well as the optimal development of existing fields, requires the integration of unconventional geological and geophysical techniques. In particular, the calibration of 3D seismic data to well log information is crucial to obtain a quantitative understanding of reservoir properties. The advent of new technology for prestack seismic data analysis and 3D visualisation has resulted in improved fluid and lithology predictions prior to expensive drilling. Increased reservoir resolution has been achieved by combining seismic inversion with AVO analysis to minimise exploration risk.In this paper we present an integrated and systematic approach to prospect evaluation in an oil/gas field. We will show how petrophysical analysis of well log data can be used as a feasibility tool to determine the fluid and lithology discrimination capabilities of AVO and inversion techniques. Then, a description of effective AVO and prestack inversion tools for reservoir property quantification will be discussed. Finally, the incorporation of the geological interpretation and the use of 3D visualisation will be presented as a key integration tool for the discovery of new plays.

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