New inversion-based rock physics method for calibration of seismic inversion products with well logs

2020 ◽  
Author(s):  
Valeriia Sobolevskaia ◽  
Carlos Torres-Verdín
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
Well Logs

Seismic petrophysics focused case study for AVA modelling and pre-stack seismic inversion

2016 ◽  
Vol 56 (1) ◽  
pp. 341
Author(s):  
Jahan Zeb ◽  
Sanjeev Rajput ◽  
Jimmy Ting
Keyword(s):  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Oil Field ◽  
Well Logs ◽  
Real Field ◽  
Seismic Responses ◽  
Reservoir Properties ◽  
Rock Types ◽  
Amplitude Versus

Hydrocarbon reservoirs are characterised by integrating seismic, well-log and petrophysical information, which are dissimilar in spatial distribution, scale and relationship to reservoir properties. Well logs are essential for amplitude versus offset (AVO) modelling and seismic inversion. The usability of well logs can be determined during wavelet estimation, seismic-to-well ties, background model building, property distribution for inversion, deriving probability density functions and variograms, offset-to-angle conversion of seismic data, and many other processes. For the implementation of seismic inversion workflows, accurate and geologically corrected compressional-sonic, shear-sonic and density logs are necessary. Preparing the logs for quantitative interpretation becomes challenging in a real-field environment because of bad borehole conditions including washouts, uncalibrated and variability of logging tools, invasion effects, missing shear logs and change of borehole size. Conventional petrophysical analysis is usually restricted to the reservoir interval, the calculation of reservoir versus non-reservoir (including sands or shales), and log corrections for smaller intervals; in contrast, seismic petrophysics encompasses the entire geological interval, calculates the volume of multi-minerals, incorporates boundaries between non-reservoir and reservoir, and often includes the prediction of missing compressional and shear-sonic for AVO analysis. A detailed seismic petrophysics analysis was performed for amplitude versus angle (AVA) modelling and attributes analysis. To perform the AVA modelling, a series of forward models in association with rock physics modelled fluid-substituted logs were developed, and associated seismic responses for various pore fluids and rock types studied. The results reveal that synthetic seismic responses together with the AVA analysis show changes for various lithologies. AVA attributes analysis show trends in generated synthetic seismic responses for various fluid-substituted and porosity logs. Reservoir modelling and fluid substitution increases understanding of the observed seismic response. This paper describes detailed data analysis using various techniques to confirm the rock model for petrophysical evaluation, rock physics modelling, AVA analysis, pre-stack seismic inversion, and the scenario modelling applied to the study of an oil field in Australia.

Seismic Quantitative Interpretation for Uncertainty Reduction of Subsurface Modeling of the Deep Visean Reservoirs

2021 ◽  
Author(s):  
Pavlo Kuzmenko ◽  
Rustem Valiakhmetov ◽  
Francesco Gerecitano ◽  
Viktor Maliar ◽  
Grigori Kashuba ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Seismic Data ◽  
Seismic Anisotropy ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Well Logs ◽  
Quantitative Interpretation ◽  
Structural Interpretation ◽  
Elastic Inversion ◽  
Subsurface Modeling

Abstract The seismic data have historically been utilized to perform structural interpretation of the geological subsurface. Modern approaches of Quantitative Interpretation are intended to extract geologically valuable information from the seismic data. This work demonstrates how rock physics enables optimal prediction of reservoir properties from seismic derived attributes. Using a seismic-driven approach with incorporated prior geological knowledge into a probabilistic subsurface model allowed capturing uncertainty and quantifying the risk for targeting new wells in the unexplored areas. Elastic properties estimated from the acquired seismic data are influenced by the depositional environment, fluid content, and local geological trends. By applying the rock physics model, we were able to predict the elastic properties of a potential lithology away from the well control points in the subsurface whether or not it has been penetrated. Seismic amplitude variation with incident angle (AVO) and azimuth (AVAZ) jointly with rock-derived petrophysical interpretations were used for stochastical modeling to capture the reservoir distribution over the deep Visean formation. The seismic inversion was calibrated by available well log data and by traditional structural interpretation. Seismic elastic inversion results in a deep Lower Carboniferous target in the central part of the DDB are described. The fluid has minimal effect on the density and Vp. Well logs with cross-dipole acoustics are used together with wide-azimuth seismic data, processed with amplitude control. It is determined that seismic anisotropy increases in carbonate deposits. The result covers a set of lithoclasses and related probabilities: clay minerals, tight sandstones, porous sandstones, and carbonates. We analyzed the influence of maximum angles determination for elastic inversion that varied from 32.5 to 38.5 degrees. The greatest influence of the far angles selection is on the density. AI does not change significantly. Probably the 38,5 degrees provides a superior response above the carbonates. It does not seem to damage the overall AVA behavior, which result in a good density outcome, as higher angles of incidence are included. It gives a better tie to the wells for the high density layers over the interval of interest. Sand probability cube must always considered in the interpretation of the lithological classification that in many cases may be misleading (i.e. when sand and shale probabilities are very close to each other, because of small changes in elastic parameters). The authors provide an integrated holistic approach for quantitative interpretation, subsurface modeling, uncertainty evaluation, and characterization of reservoir distribution using pre-existing well logs and recently acquired seismic data. This paper underpins the previous efforts and encourages the work yet to be fulfilled on this subject. We will describe how quantitative interpretation was used for describing the reservoir, highlight values and uncertainties, and point a way forward for further improvement of the process for effective subsurface modeling.

Distinguishing gas-bearing sandstone reservoirs within mixed siliciclastic-carbonate sequences using extended elastic impedance: Nile Delta — Egypt

2016 ◽  
Vol 4 (4) ◽  
pp. T427-T441 ◽  
Author(s):  
Ahmed Hafez ◽  
John P. Castagna
Keyword(s):  
Nile Delta ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Anomalous Behavior ◽  
Well Logs ◽  
High Impedance ◽  
Bright Spots ◽  
Fluid Properties ◽  
Elastic Impedance ◽  
Gas Bearing

In the Abu Madi Formation of the Nile Delta Basin, false bright spots may be misinterpreted as being indicative of hydrocarbons due to mixed clastics and carbonates. However, rock-physics analysis of well logs in a particular prospect area where such ambiguity exists suggests that attributes derived using extended elastic impedance (EEI) inversion may help identify hydrocarbons because they better show anomalous behavior in particular directions that are readily related to pore fluids and lithology. The EEI attributes calculated from well logs correlate extremely well to lithology and fluid properties, thereby differentiating amplitude anomalies caused by gas-bearing sandstones encased in shale from similar amplitudes caused by juxtaposition of high-impedance carbonates over lower impedance water-filled sandstones. Comparing seismically derived EEI attributes to well logs from a productive well and a nonproductive well indicates that seismic inversion can successfully identify lithologies such as shales, sandstones, carbonates, and anhydrite and distinguish gas-bearing from water-bearing sandstones. The technique can thus potentially be used to better delineate and risk prospects in the area, as well as assisting exploration efforts in other locations where similar ambiguities in amplitude interpretation exist.

Role of Well Logs and Seismic Data Conditioning in Improving the Imaging of Complex Reservoirs from Abu Dhabi

2021 ◽  
Author(s):  
Khalid Obaid ◽  
Muhammad Aamir ◽  
Tarek Yehia Nafie ◽  
Omar Aly ◽  
Widad Krissat ◽  
...  
Keyword(s):  
Seismic Data ◽  
Drilling Fluid ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Well Logs ◽  
Abu Dhabi ◽  
Residual Noise ◽  
Data Conditioning ◽  
Well Data

Abstract Rock physics/seismic inversion is a powerful tool that deliver information about intra-wells rocks elastic attributes and reservoir properties such as porosity, saturation and rock lithology classification. In principle, inversion is like an engine that should be fueled by proper input quality of both seismic and well data. As for the well data, sonic and density logs measure the rock properties a few inches from the borehole. Reliability of sonic transit-time and bulk density logs can be affected by large and rapid variation in the diameter and shape of the borehole cross-section, as well as the process of drilling fluid invasion. The basic assumption for acoustic well logs editing and conditioning is to use other recorded logs (not affected by bad-hole conditions) in a Multivariate-Regression Algorithm. In addition, Fluid Substitution was implemented to correct for the mud invasion that affects the acoustic and elastic properties based on the PVT data for fluid properties computation. The logs were then quality checked by multiple cross-plotting comparisons to the standard Rock-physics trends templates. As for seismic data, there are several factors affecting the quality of surface seismic data including the presence of residual noise and multiples contamination that caused improper amplitude balancing. Optimizing the seismic data processing for the inversion studies require reviewing and conditioning the seismic gathers and pre-stack volumes, guided by a deterministic seismic-to-well tie analysis after every major stage of the processing sequence. The applied processes are mainly consisting of Curvelet domain noise attenuation to attenuate residual noise. This was followed by high resolution Radon anti-multiple to attenuate residual surface multiples and Extended interbed multiple prediction to attenuate interbed multiples. In addition, Offset dependent amplitude and spectral balancing were applied to maintain the seismic amplitudes fidelity. This paper will illustrate a case from Abu Dhabi where data conditioning results improved the Hydrocarbon saturated carbonates vs brine saturated carbonate and the lithology predictions, leading to optimizing field development plans and drilling operations.

scholarly journals Pre-stack seismic inversion based on a genetic algorithm: A case from the llanos basin (Colombia) in the absence of well information

2012 ◽  
Vol 4 (5) ◽  
pp. 05-20 ◽  
Author(s):  
Edward Moncayo ◽  
Nadejda Tchegliakova ◽  
Luis Montes
Keyword(s):  
Genetic Algorithm ◽  
Rock Physics ◽  
Real Data ◽  
Seismic Inversion ◽  
Well Logs ◽  
High Porosity ◽  
Seismic Line ◽  
Porosity And Permeability ◽  
Borehole Logs ◽  
Prospective Risk

The Llanos basin is the most prolific of the Colombian basins; however few stratigraphic plays have been explored due to the uncertainty in determining the lithology of the channels. Inside a migrated 2D section, a wide channel was identified inside a prospective sandy unit of the Carbonera Formation, composed by intercalations of sand and shale levels, and considered a main reservoir in this part of the basin. However, the lithology filling the channel was unknown due to the absence of wells. To infer the channel lithology, and diminish the prospective risk a model based pre-stack seismic inversion was proposed.However, without well logs available along the line, the uncertain initial model diminishes reliance on the inversion. To circumvent this impasse, a seismic inversion with a genetic algorithm was proposed. The algorithm was tested on synthetic seismograms and real data from an area of the basin, where well logs were available. The error analysis between the expected and the inverted results, in both scenarios, pointed out a good algorithmic performance. Then, the algorithm was applied to the pre stack data of the 2D line where the channel had been identified.According to the inverted results and rock physics analysis of wells near the seismic line with comparative geology, classified the channel was described as to be filled by silt, shale and probably some levels of shaly sands, increasing the exploratory risk because this lithology has low porosity and permeability, contrary to the producing reservoirs in neighbor fields, characterized by clean sands of high porosity. The algorithm is useful in areas with few or no borehole logs.

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 2: Field data test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C205-C218 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Structural Information ◽  
Rock Physics ◽  
Velocity Model ◽  
Seismic Inversion ◽  
Well Logs ◽  
Velocity Model Building ◽  
Rock Physics Modeling ◽  
Physics Modeling

Velocity model building is the first step of seismic inversion and the foundation of the subsequent processing and interpretation workflow. Velocity model building from surface seismic data only becomes severely underdetermined and nonunique when more than one parameter is needed to characterize the velocity anisotropy. The traditional seismic processing workflow sequentially performs seismic velocity model building, structural imaging/interpretation, and lithologic inversion, modifying the subsurface model in each step without verifications against the previously used data. We have developed an integrated model building scheme that uses all available information: seismic data, geologic structural information, well logs, and rock-physics knowledge. We have evaluated the accuracy of the anisotropic model in the image space, in which structural information is estimated. The lithologic inversion results from well logs and the dynamic seismic information (amplitude versus angle) are also fed back to the kinematic seismic inversion via a cross-parameter covariance matrix, which is a multivariate Gaussian approximation to the numerical distribution modeled from stochastic rock-physics modeling. The procedure of building the rock-physics prior information and the improvements using these extra constraints were tested on a Gulf of Mexico data set. The inverted vertical transverse isotropic model not only better focused the seismic image, but it also satisfied the geologic and rock-physics principles.

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C177-C191 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Seismic Anisotropy ◽  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Additional Information ◽  
Synthetic Test ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

scholarly journals Gas Saturated Sandstone Reservoir Modeling Using Bayesian Stochastic Seismic Inversion

2020 ◽  
Vol 5 (1) ◽  
pp. 25-31
Author(s):  
Rahmat Catur Wibowo ◽  
Ditha Arlinsky Ar ◽  
Suci Ariska ◽  
Muhammad Budisatya Wiranatanagara ◽  
Pradityo Riyadi
Keyword(s):  
Scale Up ◽  
Seismic Inversion ◽  
Well Logs ◽  
Reservoir Modeling ◽  
Inversion Method ◽  
Tight Sandstone ◽  
Stochastic Inversion ◽  
A Value ◽  
Sandstone Reservoir ◽  
Stochastic Seismic Inversion

This study has been done to map the distribution of gas saturated sandstone reservoir by using stochastic seismic inversion in the “X” field, Bonaparte basin. Bayesian stochastic inversion seismic method is an inversion method that utilizes the principle of geostatistics so that later it will get a better subsurface picture with high resolution. The stages in conducting this stochastic inversion technique are as follows, (i) sensitivity analysis, (ii) well to seismic tie, (iii) picking horizon, (iv) picking fault, (v) fault modeling, (vi) pillar gridding, ( vii) making time structure maps, (viii) scale up well logs, (ix) trend modeling, (x) variogram analysis, (xi) stochastic seismic inversion (SSI). In the process of well to seismic tie, statistical wavelets are used because they can produce good correlation values. Then, the stochastic seismic inversion results show that the reservoir in the study area is a reservoir with tight sandstone lithology which has a low porosity value and a value of High acoustic impedance ranging from 30,000 to 40,000 ft /s*g/cc.

Rock physics model‐based seismic inversion

2006 ◽  
Author(s):  
Kyle Spikes ◽  
Jack Dvorkin ◽  
Gary Mavko
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
Model Based ◽  
Physics Model ◽  
Rock Physics Model
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