Prediction of Reservoir Properties from Seismic Data by Multivariate Geostatistics Analysis

2021 ◽  
Author(s):  
Vladimir V. Bezkhodarnov ◽  
Tatiana I. Chichinina ◽  
Mikhail O. Korovin ◽  
Valeriy V. Trushkin
Keyword(s):  
Seismic Data ◽  
Quantitative Estimation ◽  
Clay Content ◽  
Seismic Attributes ◽  
Information Value ◽  
Seismic Exploration ◽  
Rock Properties ◽  
Regression Equations ◽  
Reservoir Properties ◽  
Multivariate Geostatistics

Abstract A new technique has been developed and is being improved, which allows, on the basis of probabilistic and statistical analysis of seismic data, to predict and evaluate the most important parameters of rock properties (including the reservoir properties such as porosity and permeability), that is, oil saturation, effective thicknesses of reservoirs, their sand content, clay content of seals, and others; it is designed to predict the reservoir properties with sufficient accuracy and detail, for subsequent consideration of these estimates when evaluating hydrocarbon reserves and justifying projects for the deposits development. Quantitative reservoir-property prediction is carried out in the following stages: –Optimization of the graph ("scenario") of seismic data processing to solve not only the traditional structural problem of seismic exploration, but also the parametric one that is, the quantitative estimation of rock properties.–Computation of seismic attributes, including exclusive ones, not provided for in existing interpretation software packages.–Estimation of reservoir properties from well logs as the base data.–Multivariate correlation and regression analysis (MCRA) includes the following two stages: Establishing correlations of seismic attributes with estimates of rock properties obtained from well logs.Construction of multidimensional (multiple) regression equations with an assessment of the "information value" of seismic attributes and the reliability of the resulting predictive equations. (By the "informative value" we mean the informativeness quality of the attribute.)–Computation and construction of the forecast map variants, their analysis and producing the resultant map (as the most optimal map version) for each predicted parameter.–Obtaining the resultant forecast maps with their zoning according to the degree of the forecast reliability. The MCRA technique is tested by production and prospecting trusts during exploration and reserves’ estimation of several dozen fields in Western Siberia: Kulginskoye, Shirotnoye, Yuzhno-Tambaevskoye, etc. (Tomsk Geophysical Trust, 1997-2002); Dvurechenskoe, Zapadno-Moiseevskoe, Talovoe, Krapivinskoe, Ontonigayskoe, etc. (TomskNIPIneft, 2002–2013).

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

A statistical methodology for deriving reservoir properties from seismic data

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1437-1450 ◽  
Author(s):  
Frédérique Fournier ◽  
Jean‐François Derain
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Data Set ◽  
Analysis Technique ◽  
Seismic Facies Analysis ◽  
Statistical Relationships

The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.

Delineation of reservoir channels by different seismic attributes and geobody extractions for robust volumetric estimation, Saffron Field, offshore Nile Delta, Egypt

2021 ◽  
Vol 40 (7) ◽  
pp. 484-493
Author(s):  
Doha Monier ◽  
Azza El Rawy ◽  
Abdullah Mahmoud
Keyword(s):  
Seismic Data ◽  
Nile Delta ◽  
Three Dimensional ◽  
Dynamic Test ◽  
Data Interpretation ◽  
Seismic Attributes ◽  
Reservoir Properties ◽  
Root Mean Square Amplitude ◽  
Seismic Attribute ◽  
Attribute Analysis

The Nile Delta Basin is a major gas province. Commercial gas discoveries there have been proven mainly in Pleistocene to Oligocene sediments, and most discoveries are within sandstone reservoirs. Three-dimensional seismic data acquired over the basin have helped greatly in imaging and visualization of stratigraphy and structure, leading to robust understanding of the subsurface. Channel fairways serve as potential reservoir units; hence, mapping channel surfaces and identifying and defining infill lithology is important. Predicting sand distribution and reservoir presence is one of the key tasks as well as one of the key uncertainties in exploration. Integrating state-of-the-art technologies, such as including 3D seismic reflection surveys, seismic attributes, and geobody extractions, can reduce this uncertainty through recognition and accurate mapping of channel features. In this study, seismic attribute analysis, frequency analysis through spectral decomposition (SD), geobodies, and seismic sections have been used to delineate shallow Plio-Pleistocene El Wastani Formation channel fairways within the Saffron Field, offshore Nile Delta, Egypt. This has led to providing more reliable inputs for calculation of volumetrics. Interpretation of the stacked-channels complex through different seismic attributes helped to discriminate between sand-filled and shale-filled channels and in understanding their geometries. Results include more confident delineation of four distinct low-sinuosity channelized features. Petrophysical evaluation conducted on five wells penetrating Saffron reservoirs included electric logs and modular dynamic test data interpretation. The calculated average reservoir properties were used in different volumetric calculation cases. Different approaches were applied to delineate channel geometries that were later used in performing different volumetric cases. These approaches included defining channels from root-mean-square amplitude extractions, SD color-blended frequencies, and geobodies, all calculated from prestack seismic data. The different volumetric cases performed were compared against the latest field volume estimates proven after several years of production in which an area-versus-depth input showed the closest calculated hydrocarbon volumes to the actual proven field volumes.

Seismic Data Analysis for Well Performance and Production Data Forecast: Massive Low-Relief Gas Reservoir Case Study

2021 ◽  
Author(s):  
Aleksei Anatolyevich Gorlanov ◽  
Dmitrii Yurevich Vorontsov ◽  
Aleksei Sergeevich Schetinin ◽  
Aleksandr Ivanovich Aksenov ◽  
Diana Gennadyevna Ovchinnikova
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Gas Production ◽  
Poor Correlation ◽  
Well Performance ◽  
Reservoir Properties ◽  
Gas Reservoir ◽  
Water Contact ◽  
Geological Conditions ◽  
Depth Analysis

Abstract In the process of developing massive gas reservoirs, gas-water contact (GWC) rise is inevitable, which leads to water-breakthrough in wells and declining daily gas production. Drilling horizontal sidetracks and new horizontal wells helps to maintain target production levels. The direction of drilling a horizontal well section largely determines its efficiency. In complex geological conditions, a detailed analysis of seismic data in the drilling area helps to reduce drilling risks and achieve planned starting parameters. The integration of seismic data in geological models is often limited by poor correlation between reservoir properties from wells and seismic attributes. Flow simulation models use seismic data based on the assumptions made by the geological engineers. The study uses a cyclic approach to geological modeling: realizations include in-depth analysis of seismic data and well performance profiles. Modern software modules were used to automatically check the compliance of the geological realization with the development history, as well as to assess the uncertainties. This made it possible to obtain good correlation between well water cut and seismic attributes and to develop a method for determining the presence of shale barriers and "merging windows" of a massive gas reservoir with water-saturated volumes.

scholarly journals Estimation of reservoir properties from seismic data by smooth neural networks

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1969-1983 ◽  
Author(s):  
M. M. Saggaf ◽  
M. Nafi Toksöz ◽  
H. M. Mustafa
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Back Propagation ◽  
Reservoir Rock ◽  
Seismic Survey ◽  
Rock Properties ◽  
Reservoir Properties ◽  
Network Parameters ◽  
Generalization Problem ◽  
Validation Tests

The performance of traditional back‐propagation networks for reservoir characterization in production settings has been inconsistent due to their nonmonotonous generalization, which necessitates extensive tweaking of their parameters in order to achieve satisfactory results and avoid overfitting the data. This makes the accuracy of these networks sensitive to the selection of the network parameters. We present an approach to estimate the reservoir rock properties from seismic data through the use of regularized back propagation networks that have inherent smoothness characteristics. This approach alleviates the nonmonotonous generalization problem associated with traditional networks and helps to avoid overfitting the data. We apply the approach to a 3D seismic survey in the Shedgum area of Ghawar field, Saudi Arabia, to estimate the reservoir porosity distribution of the Arab‐D zone, and we contrast the accuracy of our approach with that of traditional back‐propagation networks through cross‐validation tests. The results of these tests indicate that the accuracy of our approach remains consistent as the network parameters are varied, whereas that of the traditional network deteriorates as soon as deviations from the optimal parameters occur. The approach we present thus leads to more robust estimates of the reservoir properties and requires little or no tweaking of the network parameters to achieve optimal results.

Reservoir properties prediction integrating controlled-source electromagnetic, prestack seismic, and well-log data using a rock-physics framework: Case study in the Hoop Area, Barents Sea, Norway

2017 ◽  
Vol 5 (2) ◽  
pp. SE43-SE60 ◽  
Author(s):  
Pedro Alvarez ◽  
Amanda Alvarez ◽  
Lucy MacGregor ◽  
Francisco Bolivar ◽  
Robert Keirstead ◽  
...  
Keyword(s):  
Seismic Data ◽  
Barents Sea ◽  
Rock Physics ◽  
Positive Outcome ◽  
Rock Properties ◽  
Integrated Analysis ◽  
Reservoir Properties ◽  
Data Set ◽  
Controlled Source ◽  
Rotation Scheme

We have developed an example from the Hoop Area of the Barents Sea showing a sequential quantitative integration approach to integrate seismic and controlled-source electromagnetic (CSEM) attributes using a rock-physics framework. The example illustrates a workflow to address the challenges of multiphysics and multiscale data integration for reservoir characterization purposes. A data set consisting of 2D GeoStreamer seismic and towed streamer electromagnetic data that were acquired concurrently in 2015 by PGS provide the surface geophysical measurements that we used. Two wells in the area — Wisting Central (7324/8-1) and Wisting Alternative (7324/7-1S) — provide calibration for the rock-physics modeling and the quantitative integrated analysis. In the first stage of the analysis, we invert prestack seismic and CSEM data separately for impedance and anisotropic resistivity, respectively. We then apply the multi-attribute rotation scheme (MARS) to estimate rock properties from seismic data. This analysis verified that the seismic data alone cannot distinguish between commercial and noncommercial hydrocarbon saturation. Therefore, in the final stage of the analysis, we invert the seismic and CSEM-derived properties within a rock-physics framework. The inclusion of the CSEM-derived resistivity information within the inversion approach allows for the separation of these two possible scenarios. Results reveal excellent correlation with known well outcomes. The integration of seismic, CSEM, and well data predicts very high hydrocarbon saturations at Wisting Central and no significant saturation at Wisting Alternative, consistent with the findings of each well. Two further wells were drilled in the area and used as blind tests in this case: The slightly lower saturation predicted at Hanssen (7324/7-2) is related to 3D effects in the CSEM data, but the positive outcome of the well is correctly predicted. At Bjaaland (7324/8-2), although the seismic indications are good, the integrated interpretation result predicts correctly that this well was unsuccessful.

Seismic attributes and kinematic azimuthal analysis for fracture and stress detection in complex geologic settings

2014 ◽  
Vol 2 (1) ◽  
pp. SA67-SA75 ◽  
Author(s):  
Krzysztof (Kris) Sliz ◽  
Saleh Al-Dossary
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Small Scale ◽  
Seismic Velocities ◽  
Stress Detection ◽  
Fracture Detection ◽  
Reservoir Properties ◽  
Horizontal Drilling ◽  
Compressional Stress ◽  
Orientation Density

Fractured rocks can exhibit good reservoir properties and provide high-permeability passages for hydrocarbons. Understanding fracture and stress systems is a key element in successful horizontal drilling and fracking for unconventional reservoir exploration. As a result, there is growing interest in methods that can estimate fracture orientation, density, and style. However, fracture detection using surface seismic data is challenging, and the results are usually ambiguous. Each method has its own strengths and weaknesses and responds to fractures and compressional stress in different ways. A major uncertainty in fracture analysis based on azimuthally variant seismic velocities is caused by interference from structural effects, localized small-scale velocity anomalies, and directional stress. They can induce azimuthal variation in velocity, which can mask the influence on traveltimes caused by the fractures. To overcome these challenges, we focused on a fracture and compressional stress detection methodology using 3D scanning of azimuthally dependent residual moveout volumes constrained by fracture-sensitive seismic attributes. Our workflow was successfully applied to wide-azimuth, highfold land seismic data acquired over a fractured formation in the northern part of Saudi Arabia, where we were able to map 3D zones with a high probability of fractures and differentiate them from areas with higher compressional stress.

Reservoir property prediction using abductive networks

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. P1-P9 ◽  
Author(s):  
Osama A. Ahmed ◽  
Radwan E. Abdel-Aal ◽  
Husam AlMustafa
Keyword(s):  
Statistical Methods ◽  
Seismic Data ◽  
Polynomial Regression ◽  
Seismic Attributes ◽  
Oil Field ◽  
Attribute Selection ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Porosity Logs ◽  
High Degree

Statistical methods, such as linear regression and neural networks, are commonly used to predict reservoir properties from seismic attributes. However, a huge number of attributes can be extracted from seismic data and an efficient method for selecting an attribute subset with the highest correlation to the property being predicted is essential. Most statistical methods, however, lack an optimized approach for this attribute selection. We propose to predict reservoir properties from seismic attributes using abductive networks, which use iterated polynomial regression to derive high-degree polynomial predictors. The abductive networks simultaneously select the most relevant attributes and construct an optimal nonlinear predictor. We applied the approach to predict porosity from seismic data of an area within the 'Uthmaniyah portion of the Ghawar oil field, Saudi Arabia. The data consisted of normal seismic amplitude, acoustic impedance, 16 other seismic attributes, and porosity logs from seven wells located in the study area. Out of 27 attributes, the abductive network selected only the best two to six attributes and produced a more accurate and robust porosity prediction than using the more common neural-network predictors. In addition, the proposed method requires no effort in choosing the attribute subset or tweaking their parameters.

Approaches to defining reservoir physical properties from 3-D seismic attributes with limited well control: An example from the Jurassic Smackover Formation, Alabama

Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 368-376 ◽  
Author(s):  
Bruce S. Hart ◽  
Robert S. Balch
Keyword(s):  
Seismic Data ◽  
Empirical Relationship ◽  
Real Data ◽  
Seismic Attributes ◽  
Data Sets ◽  
Small Field ◽  
Reservoir Properties ◽  
Smackover Formation ◽  
Data Volume ◽  
Seismic Sections

Much industry interest is centered on how to integrate well data and attributes derived from 3-D seismic data sets in the hope of defining reservoir properties in interwell areas. Unfortunately, the statistical underpinnings of the methods become less robust in areas where only a few wells are available, as might be the case in a new or small field. Especially in areas of limited well availability, we suggest that the physical basis of the attributes selected during the correlation procedure be validated by generating synthetic seismic sections from geologic models, then deriving attributes from the sections. We demonstrate this approach with a case study from Appleton field of southwestern Alabama. In this small field, dolomites of the Jurassic Smackover Formation produce from an anticlinal feature about 3800 m deep. We used available geologic information to generate synthetic seismic sections that showed the expected seismic response of the target formation; then we picked the relevant horizons in a 3-D seismic data volume that spanned the study area. Using multiple regression, we derived an empirical relationship between three seismic attributes of this 3-D volume and a log‐derived porosity indicator. Our choice of attributes was validated by deriving complex trace attributes from our seismic modeling results and confirming that the relationships between well properties and real‐data attributes were physically valid. Additionally, the porosity distribution predicted by the 3-D seismic data was reasonable within the context of the depositional model used for the area. Results from a new well drilled after our study validated our porosity prediction, although our structural prediction for the top of the porosity zone was erroneous. These results remind us that seismic interpretations should be viewed as works in progress which need to be updated when new data become available.

scholarly journals Unsupervised machine learning for time-lapse seismic studies and reservoir monitoring

2021 ◽  
pp. 1-59
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
David H. Johnston ◽  
Jonny Wu
Keyword(s):  
Machine Learning ◽  
Reservoir Simulation ◽  
Seismic Data ◽  
Water Saturation ◽  
Seismic Analysis ◽  
Time Lapse ◽  
Seismic Attributes ◽  
Rock Properties ◽  
Unsupervised Machine Learning ◽  
4D Seismic

Time-lapse (4D) seismic analysis plays a vital role in reservoir management and reservoir simulation model updates. However, 4D seismic data are subject to interference and tuning effects. Being able to resolve and monitor thin reservoirs of different quality can aid in optimizing infill drilling or locating bypassed hydrocarbons. Using 4D seismic data from the Maui field in the offshore Taranaki basin of New Zealand, we generate typical seismic attributes sensitive to reservoir thickness and rock properties. We find that spectral instantaneous attributes extracted from time-lapse seismic data illuminate more detailed reservoir features compared to those same attributes computed on broadband seismic data. We develop an unsupervised machine learning workflow that enables us to combine eight spectral instantaneous seismic attributes into single classification volumes for the baseline and monitor surveys using self-organizing maps (SOM). Changes in the SOM natural clusters between the baseline and monitor surveys suggest production-related changes that are caused primarily by water replacing gas as the reservoir is being swept under a strong water drive. The classification volumes also facilitate monitoring water saturation changes within thin reservoirs (ranging from very good to poor quality) as well as illuminating thin baffles. Thus, these SOM classification volumes show internal reservoir heterogeneity that can be incorporated into reservoir simulation models. Using meaningful SOM clusters, geobodies are generated for the baseline and monitor SOM classifications. The recoverable gas reserves for those geobodies are then computed and compared to production data. The SOM classifications of the Maui 4D seismic data seems to be sensitive to water saturation change and subtle pressure depletions due to gas production under a strong water drive.

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