Lithofacies Discrimination Research Articles

Lithofacies discrimination using seismic anisotropic attributes from logging data in Muglad Basin, South Sudan

Abstract The identification of formations in anisotropic reservoirs using seismic reflection data and logging data may lead to misrepresentations of the actual formations. Lithofacies discrimination intrinsically has ambiguity, and the depositional sequences of the study area comprise shales, fine-grained sands, and silts. So it needs to reduce the uncertainty of the lithofacies discrimination using anisotropic parameters. This study proposes an approach involving seismic anisotropic parameters to discriminate between different lithofacies. We calculate four anisotropic parameters (ε, δ, γ, η) from logging data (V p, V s, and density) and then employ these for lithofacies discrimination. We compared our results to lithofacies discrimination based on traditional parameters such as V p/V s ratio, clay volume, and water saturation. Using field data from Muglad Basin in South Sudan, we show how the suggested parameters could be used to identify eleven zones with distinct lithofacies. According to the anisotropic parameters, the lithofacies discrimination results are similar to other logging data, and it is easier to separate the lithofacies than petrophysical data. Furthermore, we introduce a new parameter, i.e., the difference between the normalized anisotropic δ parameter and clay volume, which can be used as a possible indicator for heavy oil reservoirs. The new parameter matches well with water saturation in the field data application.

Lithofacies discrimination of the Ordovician unconventional gas-bearing tight sandstone reservoirs using a subtractive fuzzy clustering algorithm applied on the well log data: Illizi Basin, the Algerian Sahara

The main objective of this paper is to prove the capability of the fuzzy clustering algorithm for discriminating between lithofacies that are derived from the borehole log data of Ordovician tight sandstone reservoirs in the Illizi Basin of the Algerian South-Eastern Sahara. The technique is based on applying an unsupervised machine learning fuzzy algorithm on the natural gamma-ray, the photoelectric factor, the sonic log, the neutron porosity, and the bulk density well log data as an input to discriminate lithology into four lithofacies types: clay, sand, clayey sand, and sandy clay. The primary advantage of the fuzzy algorithms is that they can handle uncertainties and errors associated with the inaccurate or incomplete data of any measurements. Furthermore, it has a broader scope and a higher level of generality than the binary logic, which means that the results and interpretations that are based on this approach can be more appropriate and probable than other possible interpretations. A comparison between our algorithm's results and the results from the Kohonen Self-organizing Maps (SOM) and the Multilayer Perceptron (MPL) has also been carried out which proved that our approach provides better results than the other commonly applied neural networks. The obtained lithofacies types are highly comparable with that obtained from the core description data with minimal estimated errors. Therefore, this fuzzy algorithm can be applied as an alternative, fast, and low-cost technique for adequate lithofacies discrimination in case of lacking the core data.

Application of prestack Poisson dampening factor and Poisson impedance inversion in sand quality and lithofacies discrimination

Application of prestack Poisson dampening factor and Poisson impedance inversion in sand quality and lithofacies discrimination

Calculation of the 1D Lithofacies Discrimination Attribute driven by Rock Physics Modelling of Maastrichtian Reservoirs from the High Block of the Roncador Field, Campos Basin

ABSTRACT. The use of impedances derived from seismic and well data is a powerful methodology aiming geophysical reservoir characterization. However, impedance calculation often lacks geological information, being up to the interpreter associate it with the computational and physical parametrizations behind the inversion process. This scenario may lead to ambiguous or even erroneous interpretations. Thus, we propose an approach of Elasto-Facies Association (EFA) driven by rock physics modelling, in an attempt to bring more geological correlation in comparison with other widely used techniques (probabilistic, unsupervised or neural networks). This analysis is based on depth trends for P-wave velocity, which, for this case, clearly revealed the dependence of elastic properties on diagenetic effects as grain sorting, cementation and mechanical compaction. Thus, we investigated how these effects impact on seismic amplitudes and elastic impedances and their correlation with petrophysical properties for reservoir and non-reservoir rocks of intervals of interest. Finally, we applied the AVOImpedance technique (AVOI), creating a new attribute based on the projection of Acoustic Impedance (AI) and Elastic Impedance (EI2), named Lithological Discrimination Attribute (LDA). It was successful in characterizing the main lithological clustering and properly discriminating the Elasto-Facies for Maastrichtian reservoirs from the High Block of the Roncador Field.Keywords: AVO analysis, seismic inversion, rock physics, reflection coefficients modelling, elastic impedances analysis. Cálculo do Atributo de Discriminação de Litofácies 1D através de Modelagem de Física das Rochas para os Reservatórios Maastrichtiano do Bloco Alto do Campo de Roncador, Bacia de CamposRESUMO. A utilização das impedâncias derivadas dos dados sísmicos e de poços gera um importante conjunto de informações que auxilia na caracterização geofísica do reservatório. Entretanto, o cálculo das impedâncias carece de integração com as informações geológicas, sendo responsabilidade do intérprete associá-las às parametrizações computacionais do algoritmo de inversão. Isso pode frequentemente produzir resultados ambíguos ou erro nas interpretações. Assim, nós propomos um estudo das Elasto-Fácies Associadas (EFA), baseado em modelos de física de rochas, atribuindo assim um modelo mais geológico do que outras técnicas largamente utilizadas (probabilística, não-supervisionada ou redes neurais). Essa análise é baseada nas tendências das velocidades de onda-P com a profundidade, o que revelou uma estreita dependência com os efeitos diagenéticos, como seleção, cimentação e compactação mecânica. A partir dessa constatação, investigamos como esses efeitos influenciam as amplitudes sísmicas, impedâncias e propriedades petrofísicas das rochas reservatório e não-reservatório dos intervalos de interesse. Por fim, aplicamos a técnica de Impedância de AVO (AVOI) gerando um atributo, baseado na projeção das impedâncias acústica (AI) e elástica (EI2), chamado Atributo de Discriminação de Litofácies (LDA). Ele obteve sucesso na caracterização dos principais agrupamentos litológicos e discriminou adequadamente as Elasto-Fácies para reservatórios Maastrichtianos do Bloco Alto do Campo de Roncador.Palavras-chave: análise de AVO, inversão sísmica, física de rochas, modelagem dos coeficientes de reflexão, análise das impedâncias elásticas.

Rock physics-driven quantitative seismic reservoir characterization of a tight gas reservoir: a case study from the Lower Indus Basin in Pakistan

Abstract The Lower Goru (LG) sandstone formation of Cretaceous age is a widely distributed proven hydrocarbon producing reservoir in the Lower Indus Basin, Pakistan. The tight early Cretaceous LG massive-sand (MAS) sandstone unit has low porosity and low permeability, which makes lithologies and litho-facies discrimination difficult. Based on a rock physics-driven quantitative seismic reservoir characterization approach, we accurately predict lithology and fluid saturations defined through litho-facies image mapping for the MAS sandstone reservoir unit. The workflow starts by preparing data for consistent petrophysical interpretation and rock physics analysis. Rock physics modelling generates a consistent set of elastic logs and establishes the link between reservoir properties and seismic amplitudes via elastic properties. Fluid substitution analysis indicates that gas sand separation from seismic data is difficult to achieve by conventional inversion methods due to very low porosity and stiffness of the rock frame which exhibits an AVO class I response. Therefore, we employed a novel joint impedance and facies inversion (Ji-Fi®) technology and jointly inverted for acoustic impedance and seismic litho-facies images. Ji-Fi produced a significantly better facies (gas sand) image and identified a potential sweet spot for a future appraisal and development well location.

Joint Inversion of 2D Gravity Gradiometry and Magnetotelluric Data in Mineral Exploration

We have developed a mineral exploration method for the joint inversion of 2D gravity gradiometry and magnetotelluric (MT) data based on data-space and normalized cross-gradient constraints. To accurately explore the underground structure of complex mineral deposits and solve the problems such as the non-uniqueness and inconsistency of the single parameter inversion model, it is now common practice to perform collocated MT and gravity surveys that complement each other in the search. Although conventional joint inversion of MT and gravity using model-space can be diagnostic, we posit that better results can be derived from the joint inversion of the MT and gravity gradiometry data using data-space. Gravity gradiometry data contains more abundant component information than traditional gravity data and can be used to classify the spatial structure and location of underground structures and field sources more accurately and finely, and the data-space method consumes less memory and has a shorter computation time for our particular inversion iteration algorithm. We verify our proposed method with synthetic models. The experimental results prove that our proposed method leads to models with remarkable structural resemblance and improved estimates of electrical resistivity and density and requires shorter computation time and less memory. We also apply the method to field data to test its potential use for subsurface lithofacies discrimination or structural classification. Our results suggest that the imaging method leads to improved characterization of geological targets, which is more conducive to geological interpretation and the exploration of mineral resources.

Comparison of the Sweetness Seismic Attribute and Porosity–Thickness Maps, Sava Depression, Croatia

The sweetness seismic attribute is a very useful tool for proper description of the depositional environment, reservoir quality and lithofacies discrimination. This paper shows that depositional channels and turbidity sandstones deposited during the Upper Pannonian and Lower Pontian in the Sava Depression can be described using porosity–thickness and sweetness seismic attribute maps. Two studied reservoirs are of Neogene stage (“UP” reservoir of Upper Pannonian age and “LP” reservoir of Lower Pontian age) and located in the Sava Depression, Croatia. Both reservoirs contain medium to fine grained sandstones that are intercalated with basinal marls. A comparison of the sweetness seismic attribute and porosity–thickness maps show a good visual match with correlation coefficient of approximately 0.85. A mismatch was observed in areas with small reservoir thickness. This work demonstrates the importance of using porosity–thickness maps for reservoir characterization. The workflow presented in this work has wider applications in frontier areas with poor seismic data or coverage.

Mixture decompositions and lithofacies clustering from wireline logs

Finite mixture models provide useful methods for modeling a wide variety of natural phenomena. Decomposing a mixture of distributions, however, has many difficulties, including separability of the component distributions, determination of the number of components, predictability of the clusters with realistic spatial patterns, and linkages between the component density functions and underlying physical processes. In this article, we use principal component analysis (PCA) to synthesize multiple wireline logs and decompose mixture populations for lithofacies clustering. The principal components of these logs characterize the rock physics; some of them contain essential information of lithofacies while others represent less relevant information or noise. In many cases, clustering based on one component is effective for decomposing the mixture and classifying lithofacies, although rotating principal components is often necessary to improve the lithofacies discrimination. In more complicated cases, PCA and direct mixture decomposition using histogram can be cascaded to decompose finite mixture models of a rock property. The proposed methodology combines the delicacy of probability theory and simplicity of linear transforms to classify lithofacies. • We present multivariate analysis of wireline logs. • Lithofacies are classified using wireline logs. • Principal component analysis is used to synthesize multiple logs and characterize rock physics of the resource formation. • Probabilistic method of histogram decomposition of rock properties is performed using principal component analysis. • We demonstrate the lithofacies classification and mixture decomposition using wireline logs with a number of examples.

ＡＬＯＳ／ＰＡＬＳＡＲデータを用いた植生地域における岩相識別

Lithofacies discrimination has been conducted using the full- and dual-polarimetric ALOS/PALSAR data in the semi-vegetated Kalgoorlie area, WA, Australia and vegetated Lao Cai area, Northern Vietnam. In the Kalgoorlie area, characteristic features of topography, vegetation and surface roughness affected by the difference of geological units were noticed from simple scatter component image of PALSAR and multi frequency image using PALSAR and ENVISAT/ASAR data. In the Lao Cai area, the relation between polarimetric characters data and lithofacies are not recognized due to the thick vegetation and steep slope, however, the geological boundaries between metamorphic, igneous and sedimentary rocks are detected by using coherence information which is computed within InSAR processing.

Joint two-dimensional cross-gradient imaging of magnetotelluric and seismic traveltime data for structural and lithological classification

SUMMARY Collocated magnetotelluric (MT) and seismic profiling is emerging as a necessary combined approach for deep and near-surface imaging but the resulting experimental data are typically interpreted separately since no production programs exist for multidimensional joint inversion of MT and seismic data. We present a joint 2-D inversion approach for imaging collocated MT and seismic refraction data with cross-gradient structural constraints. We describe the main features of the algorithm and first apply it to synthetic data generated for a hypothetical complex geological model. For the synthetic data, we find that the scheme leads to models with remarkable structural resemblance and improved estimates of electrical resistivity and seismic velocity. We apply the scheme to near-surface field data to test the consistency of a previously suggested resistivity–velocity interrelationship and its potential use for subsurface lithofacies discrimination or structural classification. The MT-seismic relationship is found to be in excellent accord with that derived previously for DC resistivity and seismic data set at the test site. Our results suggest that joint MT-seismic cross-gradient imaging leads to improved characterization of heterogeneous geological targets at near-surface to mantle depths.

Evaluation of JERS-1 (FUYO-1) OPS and Landsat TM images for mapping of gneissic rocks in arid areas

Systematic defects in images produced by the JERS-1 Optical Sensor's short wave infrared (SWIR) channels have deterred wide geological applications of the global data that JERS-1 has gathered. Frequency-domain filtering to separate noise from scene-determined signals allows the advantages of the three JERS-1 SWIR channels, covering Al-OH, Mg-OH and C-O spectral features to be assessed in relation to the widely used Landsat Thematic Mapper (TM) data. For the arid, high-grade Barka Terrane in NW Eritrea, two data-optimizing methods permit consistent and comprehensive lithofacies discrimination. These are based on: (i) pairwise principal component analysis that employs groups of two channels oriented to hydroxyl, carbonate and ferric iron species' spectral features; (ii) selection of channels to display RGB images. Optimizing this approach is based on detailed analysis of local spectroradiometry. As a general mapping tool for high-grade rocks the Landsat TM outperforms the JERS-1 Optical Sensor (OPS). However, recovery of OPS channel 8 by frequency-domain filtering uniquely permits routine identification of marbles and, together with TM band 7, allows distinction between rock surfaces whose spectra are dominated by Al-OH-and MG-OH-bearing phyllosilicates.