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.

scholarly journals Petrophysical Properties of Nahr Umar Formation in Nasiriya Oil Field

2020 ◽  
Vol 21 (3) ◽  
pp. 9-18
Author(s):  
Ahmed Abdulwahhab Suhail ◽  
Mohammed H. Hafiz ◽  
Fadhil S. Kadhim
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Oil Field ◽  
Well Logs ◽  
High Porosity ◽  
Reservoir Properties ◽  
Petrophysical Parameters ◽  
Resistivity Logs ◽  
Lithology Prediction ◽  
Porosity And Permeability

   Petrophysical characterization is the most important stage in reservoir management. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umar Formation in Nasiriya oil field. The available well logs are (sonic, density, neutron, gamma-ray, SP, and resistivity logs). The petrophysical parameters such as the volume of clay, porosity, permeability, water saturation, were computed and interpreted using IP4.4 software. The lithology prediction of Nahr Umar formation was carried out by sonic -density cross plot technique. Nahr Umar Formation was divided into five units based on well logs interpretation and petrophysical Analysis: Nu-1 to Nu-5. The formation lithology is mainly composed of sandstone interlaminated with shale according to the interpretation of density, sonic, and gamma-ray logs. Interpretation of formation lithology and petrophysical parameters shows that Nu-1 is characterized by low shale content with high porosity and low water saturation whereas Nu-2 and Nu-4 consist mainly of high laminated shale with low porosity and permeability. Nu-3 is high porosity and water saturation and Nu-5 consists mainly of limestone layer that represents the water zone.

Reservoir Prediction Under Control of Sedimentary Facies

2017 ◽  
Vol 25 (03) ◽  
pp. 1750022
Author(s):  
Xiuwei Yang ◽  
Peimin Zhu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Prediction Method ◽  
Rock Physics ◽  
Real Data ◽  
Sedimentary Facies ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Reservoir Prediction ◽  
Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

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.

A NEW OPTIMIZATION METHOD FOR ENHANCED FORMATION EVALUATION AND ROBUST PHYSICS-BASED AUTOMATIC ROCK CLASSIFICATION USING HIGH-RESOLUTION CT-SCAN IMAGE DATA AND CONVENTIONAL WELL LOGS

2021 ◽  
Author(s):  
Andres Gonzalez ◽  
◽  
Zoya Heidari ◽  
Olivier Lopez ◽  
◽  
...  
Keyword(s):  
Ct Scan ◽  
Cost Function ◽  
Bulk Density ◽  
Rock Physics ◽  
Well Logs ◽  
Petrophysical Properties ◽  
Rock Classification ◽  
Formation Evaluation ◽  
Rock Fabric ◽  
Porosity And Permeability

Conventional formation evaluation provides fast and accurate estimations of petrophysical properties in conventional formations through conventional well logs and routine core analysis (RCA) data. However, as the complexity of the evaluated formations increases conventional formation evaluation fails to provide accurate estimates of petrophysical properties. This inaccuracy is mainly caused by rapid variation in rock fabric (i.e., spatial distribution of rock components) not properly captured by conventional well logging tools and interpretation methods. Acquisition of high-resolution whole-core computed tomography (CT) scanning images can help to identify rock-fabric-related parameters that can enhance formation evaluation. In a recent publication, we introduced a permeability-based cost function for rock classification, optimization of the number of rock classes, and estimation of permeability. Incorporation of additional petrophysical properties into the proposed cost function can improved the reliability of the detected rock classes and ultimately improve the estimation of class-based petrophysical properties. The objectives of this paper are (a) to introduce a robust optimization method for rock classification and estimation of petrophysical properties, (b), to automatically employ whole-core two-dimensional (2D) CT-scan images and slabbed whole-core photos for enhanced estimates of petrophysical properties, (c) to integrate whole-core CT-scan images and slabbed whole-core photos with well logs and RCA data for automatic rock classification, (d) to derive class-based rock physics models for improved estimates of petrophysical properties. First, we conducted formation evaluation using well logs and RCA data for estimation of petrophysical properties. Then, we derived quantitative features from 2D CT-scan images and slabbed whole-core photos. We employed image-based features, RCA data and CT-scan-based bulk density for optimization of the number rock classes. Optimization of rock classes was accomplished using a physics-based cost function (i.e., a function of petrophysical properties of the rock) that compares class-based estimates of petrophysical properties (e.g., permeability and porosity) with core-measured properties for increasing number of image-based rock classes. The cost function is computed until convergence is achieved. Finally, we used class-based rock physics models for improved estimates of porosity and permeability. We demonstrated the reliability of the proposed method using whole-core CT-scan images and core photos from two siliciclastic depth intervals with measurable variation in rock fabric. We used well logs, RCA data, and CT-scan-based bulk-density. The advantages of using whole-core CT-scan data are two-fold. First, it provides high-resolution quantitative features that capture rapid spatial variation in rock fabric allowing accurate rock classification. Second, the use of CT-scan-based bulk density improved the accuracy of class-based porosity-bulk density models. The optimum number of rock classes was consistent for all the evaluated cost functions. Class-based rock physics models improved the estimates of porosity and permeability values. A unique contribution of the introduced workflow when compared to previously documented image-based rock classification workflows is that it simultaneously improves estimates of both porosity and permeability, and it can capture rock class that might not be identifiable using conventional rock classification techniques.

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.

Two-parameter prestack seismic inversion of porosity and pore structure parameter of fractured carbonate reservoirs: Part 1 — Methods

2018 ◽  
Vol 6 (4) ◽  
pp. SM1-SM8 ◽  
Author(s):  
Tingting Zhang ◽  
Yuefeng Sun
Keyword(s):  
Pore Structure ◽  
Seismic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
High Porosity ◽  
Structure Parameter ◽  
Pore Structure Parameter ◽  
Fractured Zones ◽  
Physics Model ◽  
Rock Physics Model

Fractured zones in deeply buried carbonate hills are important because they often have better permeability resulting in prolific production than similar low-porosity rocks. Nevertheless, their detection poses great challenge to conventional seismic inversion methods because they are mostly low in acoustic impedance and bulk modulus, hardly distinguishable from high-porosity zones or mudstones. A proxy parameter of pore structure defined in a rock-physics model, the so-called Sun model, has been used for delineating fractured zones in which the pore structure parameter is relatively high, whereas the porosity is low in general. Simultaneous seismic inversion of the pore structure parameter and porosity proves to be difficult and nontrivial in practice. Although the pore structure parameter is well-defined at locations where density, P-, and S-velocity are known from logs, estimation of P- and S-velocity information, especially density information from prestack seismic data is rather challenging. A three-step iterative inversion method, which uses acoustic, gradient, and elastic impedance from angle-stacked seismic data as input to the rock-physics model for calculating porosity and bulk and shear pore structure parameters simultaneously, is proposed and implemented to solve this problem. The methodology is successfully tested with well logs and seismic data from a deeply buried carbonate hill in the Bohai Bay Basin, China.

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.

Seismic well tie by aligning impedance log with inverted impedance from seismic data

2020 ◽  
Vol 8 (4) ◽  
pp. T917-T925
Author(s):  
Bo Zhang ◽  
Yahua Yang ◽  
Yong Pan ◽  
Hao Wu ◽  
Danping Cao
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Real Data ◽  
Seismic Inversion ◽  
Synthetic Seismogram ◽  
Well Logs ◽  
Time Warping ◽  
Seismic Wavelet ◽  
Dynamic Time ◽  
Depth Relationship

The accuracy of seismic inversion is affected by the seismic wavelet and time-depth relationship generated by the process of the seismic well tie. The seismic well tie is implemented by comparing the synthetic seismogram computed from well logs and the poststack seismogram at or nearby the borehole location. However, precise waveform matching between the synthetic seismogram and the seismic trace does not guarantee an accurate tie between the elastic properties contained represented by the seismic data and well logs. We have performed the seismic well tie using the impedance log and the impedance inverted from poststack seismic data. We use an improved dynamic time warping to align the impedance log and impedance inverted from seismic data. Our workflow is similar to the current procedure of the seismic well tie except that the matching is implemented between the impedance log and the inverted impedance. The current seismic well-tie converges if there is no visible changes for the wavelets and time-depth relationship in the previous and current tying loops. Similarly, our seismic well tie converges if there are no visible changes for the wavelets, inverted impedance, and time-depth relationship in the previous and current tying loops. The real data example illustrates that more accurate inverted impedance is obtained by using the new wavelet and time-depth relationship.

Seismic-based characterization of reservoir heterogeneity within the Meramec interval of the STACK play, Central Oklahoma

2020 ◽  
pp. 1-46
Author(s):  
William Neely ◽  
Ahmed Ismail ◽  
Mohammed Ibrahim ◽  
James Puckette
Keyword(s):  
Oil And Gas ◽  
Seismic Inversion ◽  
Well Logs ◽  
Reservoir Quality ◽  
High Porosity ◽  
3D Seismic ◽  
Horizontal Drilling ◽  
Wide Range ◽  
Porosity Heterogeneity

The Meramec interval within the “STACK” play of the Anadarko Basin in central Oklahoma has been recently at the epicenter of increased exploration and production of oil and gas. It has become one of the top target intervals of the “Mid-Continent” aided by the technological advancements in horizontal drilling and completion techniques. The Meramec interval, mainly composed of argillaceous siliciclastic sediments with varying amounts of carbonate cement, exhibits high porosity heterogeneity, which is theorized to be caused by varying amounts of clay and post-depositional calcite cement. Characterization of the porosity heterogeneity in the Meramec interval will improve our understanding of the wide range in Meramec oil and gas production volumes and reduce the risk associated with drilling and completion techniques. We completed an initial interpretation followed by inversion of 3D seismic data where we generated a detailed characterization of the porosity heterogeneity and overall reservoir quality within the Meramec interval over an area of approximately 150 square kilometers. We then used the 3D seismic volume and available well logs to map the vertical and lateral extents of the Meramec interval and identify possible structural elements that could affect the reservoir quality. A petrophysical analysis of the well logs confirmed porosity heterogeneity and variations in volumetric calculations of clay and carbonate minerals. Finally, we generated a set of porosity volumes using the acoustic impedance from seismic inversion and probabilistic neural network methods. The derived porosity volume helped us identify porous and non-porous intervals within the Meramec throughout the study area. The results improved our understanding of Meramec heterogeneity, further reducing the risk associated with well planning, drilling and completion.

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