Prediction of Reservoir Properties Based on Seismic Inversion Technique of Full Stack 2D Seismic Data

2020 ◽  
Author(s):  
G. Akhmetzhanova ◽  
R. Abirov ◽  
A. Zheksenbayeva
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Inversion Technique

Applications of quantitative prestack seismic analysis to unconventional resource play characterization in the Permian/Delaware Basin

2019 ◽  
Vol 38 (2) ◽  
pp. 106-115 ◽  
Author(s):  
Phuong Hoang ◽  
Arcangelo Sena ◽  
Benjamin Lascaud
Keyword(s):  
Hydraulic Fracturing ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Yield Attributes ◽  
Horizontal Drilling ◽  
Delaware Basin ◽  
Unconventional Resource

The characterization of shale plays involves an understanding of tectonic history, geologic settings, reservoir properties, and the in-situ stresses of the potential producing zones in the subsurface. The associated hydrocarbons are generally recovered by horizontal drilling and hydraulic fracturing. Historically, seismic data have been used mainly for structural interpretation of the shale reservoirs. A primary benefit of surface seismic has been the ability to locate and avoid drilling into shallow carbonate karsting zones, salt structures, and basement-related major faults which adversely affect the ability to drill and complete the well effectively. More recent advances in prestack seismic data analysis yield attributes that appear to be correlated to formation lithology, rock strength, and stress fields. From these, we may infer preferential drilling locations or sweet spots. Knowledge and proper utilization of these attributes may prove valuable in the optimization of drilling and completion activities. In recent years, geophysical data have played an increasing role in supporting well planning, hydraulic fracturing, well stacking, and spacing. We have implemented an integrated workflow combining prestack seismic inversion and multiattribute analysis, microseismic data, well-log data, and geologic modeling to demonstrate key applications of quantitative seismic analysis utilized in developing ConocoPhillips' acreage in the Delaware Basin located in Texas. These applications range from reservoir characterization to well planning/execution, stacking/spacing optimization, and saltwater disposal. We show that multidisciplinary technology integration is the key for success in unconventional play exploration and development.

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.

Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. O21-O37 ◽  
Author(s):  
Dario Grana ◽  
Ernesto Della Rossa
Keyword(s):  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Joint Estimation ◽  
Physical Models ◽  
Inversion Method ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Statistical Rock Physics

A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and linearized seismic modeling connects velocities and density to seismic amplitude. A full Bayesian approach was adopted to propagate uncertainty from seismics to petrophysics in an integrated framework that takes into account different sources of uncertainty: heterogeneity of the real data, approximation of physical models, measurement errors, and scale changes. The method has been tested, as a feasibility step, on real well data and synthetic seismic data to show reliable propagation of the uncertainty through the three different steps and to compare two statistical approaches: parametric and nonparametric. Application to a real reservoir study (including data from two wells and partially stacked seismic volumes) has provided as a main result the probability densities of petrophysical properties and litho-fluid classes. It demonstrated the applicability of the proposed inversion method.

Typical Mistakes in Seismic Inversion and Reservoir Properties Prediction in the InterwellS pace : From Well to Seismic Data

2019 ◽  
Author(s):  
A. Rzaeva ◽  
N. Farafontova ◽  
L. Kuznetsova ◽  
A. Merzlikina ◽  
M. Fedotov ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Reservoir Properties

Data-driven Prestack Waveform Inversion Using Genetic Algorithm- Methodology and Examples

2021 ◽  
pp. 1-97
Author(s):  
Lingxiao Jia ◽  
Subhashis Mallick ◽  
Cheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Seismic Inversion ◽  
P Wave ◽  
Data Driven ◽  
Initial Model ◽  
Reservoir Properties ◽  
Well Control ◽  
Seismic Waveform

The choice of an initial model for seismic waveform inversion is important. In matured exploration areas with adequate well control, we can generate a suitable initial model using well information. However, in new areas where well control is sparse or unavailable, such an initial model is compromised and/or biased by the regions with more well controls. Even in matured exploration areas, if we use time-lapse seismic data to predict dynamic reservoir properties, an initial model, that we obtain from the existing preproduction wells could be incorrect. In this work, we outline a new methodology and workflow for a nonlinear prestack isotropic elastic waveform inversion. We call this method a data driven inversion, meaning that we derive the initial model entirely from the seismic data without using any well information. By assuming a locally horizonal stratification for every common midpoint and starting from the interval P-wave velocity, estimated entirely from seismic data, our method generates pseudo wells by running a two-pass one-dimensional isotropic elastic prestack waveform inversion that uses the reflectivity method for forward modeling and genetic algorithm for optimization. We then use the estimated pseudo wells to build the initial model for seismic inversion. By applying this methodology to real seismic data from two different geological settings, we demonstrate the usefulness of our method. We believe that our new method is potentially applicable for subsurface characterization in areas where well information is sparse or unavailable. Additional research is however necessary to improve the compute-efficiency of the methodology.

Seismic data conditioning and orthotropic rock-physics-based inversion of wide-azimuth P-wave seismic data for fracture and total organic carbon characterization in a north Kuwait unconventional reservoir

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. B229-B240 ◽  
Author(s):  
Rajive Kumar ◽  
Prashant Bansal ◽  
Bader S. Al-Mal ◽  
Sagnik Dasgupta ◽  
Colin Sayers ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Seismic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
P Wave ◽  
Natural Fracture ◽  
Reservoir Properties ◽  
Fluid Saturation ◽  
Fracture Intensity

Optimization of production from unconventional reservoirs requires estimates of reservoir properties such as porosity, total organic carbon (TOC) content, clay content, fluid saturation, and fracture intensity. The porosity and TOC content help to determine reservoir quality, and the natural fracture intensity provides information important for the completion strategy. Because shale reservoirs display intrinsic anisotropy due to layering and the partial alignment of clay minerals and kerogen with the bedding plane, the minimum acceptable representation of the anisotropy of naturally fractured shale-gas reservoirs is orthotropy, in which a set of vertical compliant fractures is embedded in a vertical transverse isotropic (VTI) background medium. Full-azimuth seismic data are required to characterize such reservoirs and to invert for the anisotropic elastic properties. Orthotropic inversion uses azimuthally sectored seismic data stacked according to the incident angle. Even for high-fold acquisition, this azimuth/angle grouping can result in low-fold angle stacks. Orthotropic amplitude-variation-with-offset-and-azimuth (AVOAz) inversion requires seismic preconditioning techniques that ensure proper primary amplitude preservation, noise attenuation, and data alignment, and a workflow implemented for the construction of an orthotropic rock-physics model. This model integrates well and core data to estimate reservoir properties using the results of the AVOAz inversion. The seismic inversion results include the P- and S-impedance and parameters quantifying the azimuthal anisotropy. The rock model assumes a VTI kerogen-rich layer, containing aligned vertical fractures, and it uses prestack orthotropic AVOAz inversion results to predict porosity, TOC, and fracture intensity.

scholarly journals Joint Learning for Spatial Context-based Seismic Inversion of Multiple Datasets for Improved Generalizability and Robustness

Geophysics ◽  
2021 ◽  
pp. 1-50
Author(s):  
Ahmad Mustafa ◽  
Motaz Alfarraj ◽  
Ghassan AlRegib
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Seismic Inversion ◽  
Spatial Context ◽  
Reservoir Properties ◽  
Convolutional Network ◽  
Joint Learning ◽  
Multiple Datasets ◽  
Spatial Consistency ◽  
Seismic Volumes

Seismic inversion plays a very useful role in detailed stratigraphic interpretation of migrated seismic volumes by enabling the estimation of reservoir properties over the complete volume. Traditional and machine learning-based seismic inversion workflows are limited to inverting each seismic trace independently of other traces to estimate impedance profiles, leading to lateral discontinuities in the presence of noise and large geological variations in the seismic data. In addition, machine learning-based approaches suffer the problem of overfitting if there is only a small number of wells on which the model is trained. We propose a two-pronged strategy to overcome these problems. We present a temporal convolutional network that models seismic traces temporally. We further inject spatial context for each trace into its estimations of the impedance profile. To counter the problem of limited labeled data, we also present a joint learning scheme whereby multiple datasets are simultaneously used for training, sharing beneficial information among each other. This results in the improvement in generalization performance on all datasets. We present a case study of acoustic impedance inversion using the open-source SEAM and Marmousi 2 datasets. Our evaluations show that our proposed approach is able to estimate impedance in the presence of noisy seismic data and a limited number of well logs with greater robustness and spatial consistency. We compare and contrast our approach to other learning-based seismic inversion methodologies in the literature. On SEAM, we are able to obtain an average MSE of 0.0476, the lowest among all other methodologies.

Application of Seismic Inversion Technique Base on Geostatistics in Prediction of Thin Sand Body Reservoir

2014 ◽  
Vol 1030-1032 ◽  
pp. 724-727
Author(s):  
Chun Lei Li ◽  
Wen Qi Zhang ◽  
Zhao Hui Xia ◽  
Ming Zhang ◽  
Liang Chao Qu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Inversion Method ◽  
Reservoir Model ◽  
Reservoir Prediction ◽  
Inversion Technique ◽  
Pulse Inversion ◽  
Sand Body ◽  
Geostatistical Inversion

Seismic inversion methods include constrained sparse pulse inversion and band limit inversion, etc. Although resolution of the seismic inversion results is higher than seismic data, it does not identify thin interbedding sand body and confirm the development of reservoirs. In this paper, in A block of Indonesia adopted geostatistical inversion in reservoir prediction, which is a method of seismic inversion combining geological statistics simulation and seismic inversion. This inversion method can establish various 3D geological model with the same probability of rock properties and lithology and it obey all seismic, logging and geological data. Using statistical regularity and seismic inversion technique we can obtain more fine reservoir model and finally reach the purpose of identification of single thin sand layer.

Seismic inversion combining rock physics and multiple-point geostatistics

Geophysics ◽  
2008 ◽  
Vol 73 (1) ◽  
pp. R11-R21 ◽  
Author(s):  
Ezequiel F. González ◽  
Tapan Mukerji ◽  
Gary Mavko
Keyword(s):  
Rock Physics ◽  
Real Data ◽  
Seismic Inversion ◽  
Multiple Point ◽  
Reservoir Properties ◽  
Inference Problem ◽  
Log Data ◽  
Inversion Technique ◽  
Data Application ◽  
Synthetic Test

A novel inversion technique combines rock physics and multiple-point geostatistics. The technique is based on the formulation of the inverse problem as an inference problem and incorporates multiple-point geostatistics and conditional rock physics to characterize previously known geologic information. The proposed implementation combines elements of sampling from conditional probabilities and elements of optimization. The technique provides multiple solutions, all consistent with the expected geology, well-log data, seismic data, and the local rock-physics transformations. A pattern-based algorithm was selected as the multiple-point geostatistics component. Rock-physics principles are incorporated at the beginning of the process, defining the links between reservoir properties (e.g., lithology, saturation) and physical quantities (e.g., compressibility, density), making it possible to predict situations not sampled by log data. Results for seismic lithofacies inversion on a synthetic test and a real data application demonstrate the validity and applicability of the proposed inversion technique.

Accurate Pore Fluid Indicator Prediction Using Seismic Fluid Bulk Modulus Inversion

2021 ◽  
Author(s):  
Makky Sandra Jaya ◽  
Ghazali Ahmad Riza ◽  
Ahmad Fuad M. Izzuljad ◽  
Mad Sahad Salbiah
Keyword(s):  
Bulk Modulus ◽  
Seismic Data ◽  
High Probability ◽  
Quantitative Measure ◽  
Seismic Inversion ◽  
Pore Fluid ◽  
Blue Color ◽  
Direct Estimation ◽  
Inversion Technique ◽  
Fluid Parameter

Submitted Abstract Objectives/Scope The prediction of fluid parameter related to hydrocarbon presence using seismic data has often been limited by the performance of probability density function in estimating fluid properties from seismic inversion results. A novel fluid bulk modulus inversion (fBMI) is a pre-stack seismic inversion technique that has been developed to allow a direct estimation of pore fluid bulk modulus (Kf) from seismic data. Real data application in Malay basin showcases that Kf volume can be used to pinpoint areas with high probability of hydrocarbon presence. Methods, Procedures, Process The fluid term AVO reflectivity (Russell et al., 2011) is used as the basis of our formulation and has been extended to allow direct estimation of pore fluid bulk modulus, shearmodulus, porosity parameter and density through standard least-square inversion. The novel formulation is able to relax the dependency of fluid terms on the porosity. To demonstrate this, verifications were made against standard linear AVO approximations. Our observation shows that the young tertiary basins such as the Malay basin the fluid bulk modulus values have a big contrast between hydrocarbon saturated and water bearing reservoirs with a minimum of 60% ratio difference. The inverted fluid bulk modulus volume provides thus a direct assessment of areas with high probability of hydrocarbon saturation. Results, Observations, Conclusions In this paper, the fBMI technique is showcased on a field in the Malay basin. The outcome is demonstrated on a well panel analysis for four wells located across the study area (Figure 1). The inverted fluid bulk modulus extracted along a horizon representing the top of target reservoir is shown in Figure 2b. The blue color indicates high bulk modulus corresponds to water-bearing zone, while the yellow-red color range corresponding to low hydrocarbon-bearing zones. The areas of low fluid bulk modulus values at the north-western region are calibrated to known production zones in that region. fBMI shows areas that delineate high probability of hydrocarbon presence and provides a quantitative measure in terms of fluid parameter directly related to the presence of hydrocarbon saturations. Figure 1: Comparison analysis of water saturation (blue curve) and fluid bulk modulus (red curve) of well log data in the Malay basin. Black strips indicate the coal intervals. Figure 2: a) Inverted acoustic impedance extracted from the top reservoir horizon of a field in the Malay basin. b) The corresponding fluid bulk modulus values from fBMI. Novel/Additive Information The fBMI is a new four parameters linear amplitude-versus-offset inversion technique that provides quantitative fluid parameter directly related to fluid bulk modulus from seismic data. It is utilized as a tool for direct hydrocarbon prospect assessment to differentiate gas, oil, condensate and water.

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