Seismic Lithofacies Research Articles

Three-dimensional seismic architecture of an Upper Cretaceous volcanic complex and associated carbonate systems; Taylor Group, Elaine field, South Texas, USA

The seismic architecture of a tuff-dominated volcanic complex and the associated carbonate systems in the Upper Cretaceous Taylor Group (Anacacho Limestone) Elaine field in South Texas was examined using a three-dimensional seismic dataset. The investigation demonstrates that the combination of traditional seismic lithofacies analysis and seismic sedimentology allows the visualization and interpretation of volcanic elements and penecontemporaneous carbonate lithofacies at the scale of tens of meters. A wireline-log based, rock-physics model was analyzed and calibrated using core samples, which link volcanic and carbonate lithofacies to 90°-phase seismic amplitude events. Seismic-velocity effects permit the identification of subtle volcanic lithofacies changes such as low-velocity tuff bodies or high-velocity basalt intrusions. Also, frequency fusion expands seismic visualization of thick lithofacies bodies (e.g., basaltic dykes) to the 100-m range and seismic facies analysis helps understand interrelationships between the components in and adjacent to the volcanic body. Horizontal seismic slices reveal seismic geomorphology at multiple stages of volcanic activity. This investigation reveals three major seismic facies systems (1) a magma-feeder (i.e., plumbing) intrusive system beneath the volcano, including deep faults, dykes and sills, and remnant radial/concentric fault systems; (2) a large (4000–5000 m diameter) volcanic complex consisting of a deep crater (300-m negative feature below the paleo-sea floor), a large mound (200-m positive feature above the paleo-sea floor), multiple secondary craters filled predominantly with tuffs and less common lava flows; and (3) penecontemporaneous carbonate systems including fringing and mound-top shoals and reefs, lagoon and debris fan aprons at the base of the volcanic mounds. The fringing and mound top carbonate systems were probably in water depths less than 20 m whereas for the fan-apron carbonates, water depths are estimated to be about 140–200 m. These carbonate bodies can form hydrocarbon reservoirs.

New method for the reconstruction of sedimentary systems including lithofacies, environments, and flow paths: A case study of the Xisha Trough Basin, South China Sea

Existing seismic facies analysis schemes, including traditional seismic facies analysis, seismic slice analysis, and automatic classification of seismic facies, can reconstruct the sedimentary systems of basins; however, they are not ideal for basins with few boreholes and many rock types. Herein, we propose a new method for reconstructing sedimentary systems using seismic data, considering the Xisha Trough Basin in the Northern South China Sea as the research object to demonstrate the principles, methods, and technologies of this novel scheme. Based on the six commonly used seismic parameters, three additional seismic parameters (smoothness, neatness, and special waveform) and one geological marker (provenance) were added to improve the recognition of lithofacies. The scanned seismic interpretation technique was used to describe the distribution of sandstone and other skeleton lithofacies; sedimentary environments were determined according to the coastline, shelf break line, and slope toe line; flow path systems were determined according to the paleotopography, incised valleys, and sandstone distribution; and the sedimentary systems of the trough basin were reconstructed through the superposition of skeleton lithofacies, depositional environments, and flow paths. Results show that the sedimentary system reconstructed using the new method contains three elements, namely, lithofacies, the environment, and flow path, which fully reflect the concept of the sedimentary system defined by Fisher and McGowen (1967) ; this overcomes the inherent defects of previous schemes, such as poor lithofacies recognition and the uncertainty of types and boundaries of sedimentary bodies. This method enabled identifying subdivided seismic lithofacies directly from seismic profiles and enhanced the ability to obtain sedimentary information from seismic data. The proposed reconstruction method can be used in areas without boreholes or with many rock types and is significant for marine geology research and oil and gas exploration conducted in the deep sea.

Deep learning network optimization and hyperparameter tuning for seismic lithofacies classification

Deep learning is increasingly being applied in many aspects of seismic processing and interpretation. Here, we look at a deep convolutional neural network approach to multiclass seismic lithofacies characterization using well logs and seismic data. In particular, we focus on network performance and hyperparameter tuning. Several hyperparameter tuning approaches are compared, including true and directed random search methods such as very fast simulated annealing and Bayesian hyperparameter optimization. The results show that improvements in predictive capability are possible by using automatic optimization compared with manual parameter selection. In addition to evaluating the prediction accuracy's sensitivity to hyperparameters, we test various types of data representations. The choice of input seismic data can significantly impact the overall accuracy and computation speed of the optimized networks for the classification challenge under consideration. This is validated on a 3D synthetic seismic lithofacies example with acoustic and lithologic properties based on real well data and structure from an onshore oil field.

Lithofacies-dependent rock physics templates of an unconventional shale reservoir on the North Slope, Alaska

Over the past 20 years, oil and gas companies have turned their attention to producing petroleum directly from organic-rich shale. Successful exploration, appraisal, and production strategies for source rocks critically depend on reliable identification of their organic components (kerogen, in particular) and generation potential. There is mounting demand to evaluate organic richness in terms of quantity (i.e. total organic carbon) and quality (i.e. hydrogen index) from seismic data, which is usually the only source of information in the early development period of emerging shale plays. We delineated major seismic lithofacies on the Alaska North Slope using elastic, seismic, and petrophysical properties. We performed a well-established quantitative seismic interpretation workflow to integrate geochemical data in the lithofacies definition. Rock physics templates of seismic parameters, Acoustic Impedance, (AI), versus P-wave to S-wave velocity ratio, (VP/VS), are constructed for each lithofacies to assess variations in pore fluid and lithology. We proposed correlations between source rock properties (hydrogen index, total organic carbon) and petrophysical properties (bulk density, porosity, sonic velocity ratio) of the major lithofacies. These correlations, together with facies-specific rock physics templates, can be utilized to predict organic richness and source rock properties away from drilled wells. The models are validated by training data from 2 regional wells to observe their applicability on the Alaska North Slope.

Facies Identification Based on Multikernel Relevance Vector Machine

Facies identification is a powerful means to predict reservoirs. We achieve facies identification using a relevance vector machine (RVM) and develop a facies discriminant method based on a multikernel RVM (MKRVM). An RVM has the same functional form as a support vector machine (SVM) that is widely used in geophysics and shows a promising performance in disposing of small-samples, nonlinear and high-dimensional problems. The RVM inherits these superiorities, and its training is implemented under the Bayesian framework. Thus, it can provide probability information about the classified facies, which is critical to evaluate uncertainty of the result. Besides, the penalty parameter of the RVM does not depend on human experience. Compared with single-kernel learning, multikernel learning (MKL) is more flexible. After mapping the original data into a combined space by MKL, the features can be more accurately expressed in the new space, thereby improving the classification accuracy. Therefore, we introduce the RVM into facies classification and extend it to the MKRVM-based facies identification. The proposed method has advantageous properties such as strong generalization ability and high accuracy. First, we apply the approach to well log facies classification with different input features. Then, it is applied to seismic lithofacies identification with inverted elastic attributes to predict the target reservoirs. All the examples verify the effect and potential of the new method.

Hydrocarbon reservoir characterization and discrimination using well-logs over “AIB-EX” Oil Field, Niger Delta

A computerized advanced statistical analysis which involves the characterization of reservoir elements involving mapping of lithofacies and pore fluids through crossplots of basic seismic variables in both bi-variate and tri-variate domains and functional transformations including rotation of axes have been used as discriminant tools over “AIB-EX” Oil Field, Niger Delta.The methodology encompasses reconstruction of geologic lithofacies information from geophysical logs. Reservoir characterization, rock physics analysis and log inversion were carried out using IHS Kingdom Advanced and Origin software. Three reservoir zones namely A, B, and C were analyzed.The obtained results characterized the reservoir elements as: shale, sandy-shale, shaly-sand and sand (with respective GR counts and P-wave velocity of 105–125 API and 2400–3600 m/s, 75–105 API and 2100–5000 m/s, 45–75 API and 2200–4750 m/s, and 10–45 API and 2000–4600 m/s) which represents seismic scale sedimentary units called lithofacies. Also, the results of both the bi-variate crossplots (GR and P-wave velocity) and tri-variate crossplots (GR, P-wave velocity, and resistivity) have not only differentiated the different lithology but have discriminated the saturating fluid (water or hydrocarbon). The pore fluids were further characterized as either brine or oil based on powerful discriminant tools such as plots of acoustic impedance versus porosity and elastic impedance versus porosity.Conclusively, the result of the research confirmed that hydrocarbon reservoirs can be discriminated with varying degree of effectiveness in various domains using the adopted approach. The obtained result, which can also be used to calibrate seismic inversion, yielded a reliable seismic lithofacies map in the presence of high resolution 3-D seismic data.

Integrated 3D facies modeling of the Mangahewa Formation, Maui Gas Field, Taranaki Basin, New Zealand

3D seismic data, well logs, core-based lithofacies and photographs have been combined to interpret and model the depositional facies of the Mangahewa Formation of the Maui Gas Field, Taranaki Basin, New Zealand. The primary objective of the study is to generate a robust facies model for the Middle to Late Eocene (47–37 Ma) Mangahewa Formation of the field. The facies model has included eighteen depositional facies spatially distributed over the gas field. These facies are further subgrouped into three broad depositional facies associations, namely marginal marine, shallow marine and offshore environment. We have identified that marginal marine is the most dominant facies association (64%) within the model. The model visualizes estuarine and shoreface sand geobodies dominating over other facies within the model. Both geobodies comprise over 40% of all the facies interpreted in the field. The entire modeling process involves a novel stochastic approach using unique workflow that follows 3D gridding, coding of the facies classes and multiple iterations over the interpreted facies. The model therefore realistically visualizes potential facies responsible for “good”-quality reservoir sands in the Mangahewa Formation with possible retrogradation from older to younger succession.

A Fast Approximation for Seismic Inverse Modeling: Adaptive Spatial Resampling

Seismic inverse modeling, which transforms appropriately processed geophysical data into the physical properties of the Earth, is an essential process for reservoir characterization. This paper proposes a work flow based on a Markov chain Monte Carlo method consistent with geology, well-logs, seismic data, and rock-physics information. It uses direct sampling as a multiple-point geostatistical method for generating realizations from the prior distribution, and Metropolis sampling with adaptive spatial resampling to perform an approximate sampling from the posterior distribution, conditioned to the geophysical data. Because it can assess important uncertainties, sampling is a more general approach than just finding the most likely model. However, since rejection sampling requires a large number of evaluations for generating the posterior distribution, it is inefficient and not suitable for reservoir modeling. Metropolis sampling is able to perform an equivalent sampling by forming a Markov chain. The iterative spatial resampling algorithm perturbs realizations of a spatially dependent variable, while preserving its spatial structure by conditioning to subset points. However, in most practical applications, when the subset conditioning points are selected at random, it can get stuck for a very long time in a non-optimal local minimum. In this paper it is demonstrated that adaptive subset sampling improves the efficiency of iterative spatial resampling. Depending on the acceptance/rejection criteria, it is possible to obtain a chain of geostatistical realizations aimed at characterizing the posterior distribution with Metropolis sampling. The validity and applicability of the proposed method are illustrated by results for seismic lithofacies inversion on the Stanford VI synthetic test sets.

Reservoir prediction of deep-water turbidite sandstones with seismic lithofacies control —A case study in the C block of lower Congo basin

Currently, conventional forecasting methods of well-to-seismic integration are unable to identify turbidite channel sandstones due to scarcity of well data in deepwater areas, small geophysical differences between sandstones and mudstones of turbidite channels and strong sandstones heterogeneity. The reservoir prediction of deep-water turbidite channels is still a difficult issue in deep-water research. On the basis of previous studies, we propose a new technology named “reservoir prediction of deep-water turbidite sandstones with seismic lithofacies control” in view of the characteristics of deep-water turbidite sandstones. This new technology improves the reservoir prediction of complex sedimentary systems after classifying seismic lithofacies and connecting lithofacies with rock-physics. Furthermore, it can accomplish the genetic classification statistics of rock-physics, improve conversion accuracy of seismic elastic parameters/reservoir parameters and achieve the quantitative reservoir prediction under the double control of seismic geomorphology and seismic lithofacies. The C block of Lower Congo Basin is characterized by few well data, complex lithology but high resolution-seismic. We use the technology to predict the reservoirs of this area and have achieved excellent results. This has great significance for the later exploration.

From qualitative to quantitative interpretation: An interpreter’s guide to fluid prediction in Pliocene to Turonian deepwater turbidites from West Africa to Asia Pacific

To mitigate exploration risk in deepwater settings, subsurface analysis increasingly has to rely on integration of qualitative with quantitative techniques. To predict pay in turbidite sandstones, proven statistical and analytical methods can routinely be run on well and seismic inversion data. However, quantitative interpretation (QI) should begin with a responsible audit of available well logs and seismic data, succeeded by data conditioning, proceeding with quality control, and placing elastic attribute responses within their geologic context. To address these issues, we evaluate geologic controls on porosity change as manifested by overpressure and compaction on calibration and analysis of elastic attributes. Following calibration of seismic inversion data, we provide tutorial-style interpretations of deepwater clastic reservoirs from the Gulf of Guinea, West Africa, to the Sabah trough, Borneo. Case study examples offer interpreters the potential to use workflows surrounding data mining in exploration or during field development. In our first example, a comparison of univariate statistics run on compressional- and shear-wave impedances and Poisson’s ratio is introduced to potentially data mine 3D seismic over turbidite fairways. Joint interpretation of P-wave and S-wave impedances is combined with innovative uses of bivariate statistical analysis for anomaly detection. Additionally, the geologic rationale of interpreting elastic relationships of calibrated attributes, such as Lambda Rho and Mu Rho, is discussed on the seismic scale of a single reservoir layer using a combination of statistical methods and rock physics. Here, qualitative interpretation, via application of principles from seismic stratigraphy and seismic geomorphology, ultimately unlocks ambiguity in rock-physics-driven, quantitative lithology determination, guiding application of QI routines toward correctly predicting the prevailing fluid type. Elastic calibration permits seismic lithofacies classification of Cretaceous turbidite sandstones deposited as middle to lower slope channels canyon-fill and basin-floor channel complexes.

Seismic inversion combining rock physics and multiple-point geostatistics

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.

Seismic reservoir mapping from 3‐D AVO in a North Sea turbidite system

We present a methodology for estimating uncertainties and mapping probabilities of occurrence of different lithofacies and pore fluids from seismic amplitudes, and apply it to a North Sea turbidite system. The methodology combines well log facies analysis, statistical rock physics, and prestack seismic inversion. The probability maps can be used as input data in exploration risk assessment and as constraints in reservoir modeling and performance forecasting. First, we define seismic‐scale sedimentary units which we refer to as seismic lithofacies. These facies represent populations of data (clusters) that have characteristic geologic and seismic properties. In the North Sea field presented in this paper, we find that unconsolidated thick‐bedded clean sands with water, plane laminated thick‐bedded sands with oil, and pure shales have very similar acoustic impedance distributions. However, the [Formula: see text] ratio helps resolve these ambiguities. We establish a statistically representative training database by identifying seismic lithofacies from thin sections, cores, and well log data for a type well. This procedure is guided by diagnostic rock physics modeling. Based on the training data, we perform multivariate classification of data from other wells in the area. From the classification results, we can create cumulative distribution functions of seismic properties for each facies. Pore fluid variations are accounted for by applying the Biot‐Gassmann theory. Next, we conduct amplitude‐variation‐with‐offset (AVO) analysis to predict seismic lithofacies from seismic data. We assess uncertainties in AVO responses related to the inherent natural variability of each seismic lithofacies using a Monte Carlo technique. Based on the Monte Carlo simulation, we generate bivariate probability density functions (pdfs) of zero‐offset reflectivity [R(0)] versus AVO gradient (G) for different facies combinations. By combining R(0) and G values estimated from 2‐D and 3‐D seismic data with the bivariate pdfs estimated from well logs, we use both discriminant analysis and Bayesian classification to predict lithofacies and pore fluids from seismic amplitudes. The final results are spatial maps of the most likely facies and pore fluids, and their occurrence probabilities. These maps show that the studied turbidite system is a point‐sourced submarine fan in which thick‐bedded clean sands are present in the feeder‐channel and in the lobe channels, interbedded sands and shales in marginal areas of the system, and shales outside the margins of the turbidite fan. Oil is most likely present in the central lobe channel and in parts of the feeder channel.

Abstract: Seismic Lithofacies in North Sea Deep Water Clastic Systems

Abstract: Seismic Lithofacies in North Sea Deep Water Clastic Systems

Seismic Modeling and Geostatistical Lithofacies Simulation of an Outcrop Case Study: A Step Towards the Integration of Seismic Data in Reservoir Simulations: ABSTRACT

A synthetic section interpreted in lithofacies has been constructed from the study of a large outcrop in order to test on a {open_quotes}true{close_quotes} geological model different methods and tools in the field of the seismic processing and geostatistical reservoir modeling. The large outcrops of the Campanian deltaic series of Colorado were studied to build a 6 km long and 350 m thick 2D section. The section was interpreted in sedimentary facies then digitized in order to compile a high-resolution grid for both geophysical and geostatistical simulations. Eleven facies where then determined in offshore, shoreface, foreshore and coastal plain environments. Rock properties such as P - wave, S - wave velocity and density were assigned to each cells of the model, and the target was buried. A fully elastic seismic modeling was then computed, and data post- processing included wavelet extraction, stratigraphic modeling and stratigraphic inversion. Pre - stack and post - stack inversions have also been performed. In the pre - stack inversion, the variation of the reflection coefficient amplitudes versus offset is taken into account, and, as consequence, resolution is very much improved. A geostatistical analysis as also performed on the same data - set, proportion curves of lithofaciesmore » and variograms were computed, and different stochastic approaches ere tested. The next step will be the computation of geostatistical parameters on seismic data in order to better constrain stochastic reservoir simulations with seismic data.« less

Seismic Facies, Lithofacies, and Preservation Potential of Incised Valley-Fill Lithosomes: Quaternary Models from the North Carolina Continental Margin

Seismic Facies, Lithofacies, and Preservation Potential of Incised Valley-Fill Lithosomes: Quaternary Models from the North Carolina Continental Margin