Seismic Volume Research Articles

Semi-Supervised Seismic Stratigraphic Interpretation Constrained by Spatial Structure

Seismic stratigraphic interpretation plays an important role in geophysics and geoscience. Recently, deep learning has been widely applied to seismic stratigraphic interpretation. These deep learning-based stratigraphic interpretation methods have shown greater potential than traditional methods. Despite the promising results achieved by deep learning-based methods, it is still necessary to enhance their generalization capabilities and the reasonability of stratigraphic interpretation. Therefore, we propose a semi-supervised deep learning-based method to improve the accuracy and reasonability of interpretation results. First, we quantitatively describe the correlation of adjacent seismic data using the dynamic time warping algorithm. The correlation of all seismic data can be regarded as the spatial structure of seismic data. The interpretation results of seismic data should conform to such spatial structure. Then, we train a deep learning model to interpret seismic stratigraphic units under the constraints of seismic spatial structure. The performance of the proposed seismic stratigraphic interpretation method is evaluated on the Netherlands F3 data. We build two scenarios to interpret the stratum: 1D scenario for the one seismic profile and 2D scenario for the whole seismic volume. The results on the field data demonstrate that the proposed method has better generalization ability and the interpretation results are more reasonable. Therefore, the proposed method can be a useful tool for seismic stratigraphic interpretation.

Improving the Generalization of Deep Neural Networks in Seismic Resolution Enhancement

Seismic resolution enhancement is a key step for subsurface structure characterization. Although many have proposed the use of deep learning (DL) for resolution enhancement, these are typically hindered by the limitations in the application of synthetically trained networks onto real datasets. Domain adaptation (DA) offers an approach to reduce this disparity between training and inference data, aiming through the application of data transformations to bring the distributions of both data closer to each other. We propose a simple DA procedure, termed MLReal-Lite (the light version of the earlier introduced MLReal), that mainly relies on linear operations, namely convolution and correlation; these transformations introduce aspects of the field data into the synthetic data prior to training, and vice-versa with regard to the inference stage. Taking 1-D and 2-D resolution enhancement tasks as examples, we show how the inclusion of MLReal-Lite improves the performance of neural networks. Not only do the results demonstrate notable improvements in seismic resolution, they also exhibit a higher signal-to-noise ratio (SNR) and better continuity of events, in comparison to the tests without MLReal-Lite. Finally, while illustrated on a resolution enhancement task, our proposed methodology is applicable for any seismic data of dimensions N-D, offering a DA applicable from well ties through to 3-D seismic volumes, and beyond.

A Multi-Task Learning Method for Relative Geologic Time, Horizons, and Faults With Prior Information and Transformer

Horizon extraction and fault detection are essential in seismic interpretation and closely related to each other. Most existing methods tend to deal with these two tasks independently, and may not work well in interpreting seismic images with complex geologic structures. We propose a multi-task learning (MTL) network with two branches to extract all horizons and detect faults simultaneously by estimating a relative geologic time (RGT) map as well as computing a fault map. These two branches share training datasets, feature maps, and network parameters during the training. The RGT estimation branch, constructed with a transformer architecture, is more lightweight compared to previous CNN methods but provides a larger and structure-oriented receptive field to adaptively capture global structural information for estimating a globally optimal RGT map. The fault detection branch is a simple convolutional neural network (CNN) which merges feature maps shared by the transformer and the derivatives of the estimated RGT to compute a fault map. The fault detection branch provides boundary control for the RGT estimation branch while the later provides global constraints for the former to improve its robustness to noise. Note that our RGT estimation by globally fitting all structures in a seismic image is a volumetric horizon interpretation method with which we are able to obtain a whole volume of horizons, all at once, by simply extracting contours of the RGT map. In our method, we further enable convenient human interactions by integrating manually interpreted horizons (or horizon segments) into the network, which imposes expert knowledge on the network to estimate reasonable RGT results from seismic images with complex fault systems, unconformities, and poor data quality. Moreover, when using 3D horizons as constraints, we are able to decompose the computationaly expensive 3D RGT estimation from a seismic volume into independently parallel 2D estimations slice by slice and combine them to obtain a laterally consistent 3D result.

Hydrocarbon Production Induced Faulting in Onshore Fuba Field Niger-Delta, Nigeria, Using 3-D Seismic Time-lapse Data

Hydrocarbon Production Induced Faulting in Onshore Fuba Field Niger Delta, Nigeria, are here presented, using 3D seismic time-lapse data. The FUBA Field lies on latitudes 40 50’58’’-4 0 55’19’’N and longitudes 60 18’41’’- 6 0 26’41’’E with aerial extent of 840km2 . The base (1997) and the monitor (2009) seismic surveys resulted in a 4D response difference. The Base and Monitor data have a root-mean-square repeatability ratio (RRR) of 0.38 implying a very good repeatability when considering the acquisition, processing and environmental noises. Data processing and interpretation were carried out using Petrel software. Reservoir pressure decline rate of 0.062psi/day resulted in production decline rate of 1192.21bbl/day. Structural interpretation of seismic data reveals a highly faulted field. Two distinct horizons were mapped. Fault and horizon interpretation shows closures that are collapsed crestal structures bounded by two major faults. All the interpreted faults are normal synthetic and antithetic faults which are common in the Niger Delta basin. The depth structure maps reveal anticlinal faults. Reservoirs are found at a shallower depth from 6500 to 7500 ft and at a deeper depth ranging from 11500 to 13000 ft. The variance edge enhanced the faults or sedimentological bodies within the seismic data volume. There are more discontinuities in the difference volume variance edge which implies that there are more cracks in the field of study due to production. The lengths, dips and orientations of the faults and horizons, in the base and monitor stacks, are not equal indicative of faults reactivation that could have resulted from hydrocarbon production. The results of the work can be applied in the hydrocarbon exploitation scheme to minimize the damages associated with production and to ascertain reactivation of faults in the area of study.

Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

Underground porosity is important in many earth sciences and engineering fields, including hydrocarbon reservoir characterization and geothermal energy production. Popular methods largely rely on the analysis of lithological core data, well logs, and seismic inversion methods. While these methods are reliable, they are also time-consuming, expensive, and difficult to implement. In addition, seismic inversion has nonlinearity, data dimensionality, and non-uniqueness issues. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability by mapping directly from seismic attributes to underground porosity. Therefore, we trained several DL models with different optimization functions. In the training steps, we labelled every seismic attribute data point with its corresponding porosity derived from the well-logs. In contrast to popular ensemble techniques, we proposed a weighted prediction approach based on the strengths of each model. Testing results showed a coefficient of determination ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{R}^{2}$ </tex-math></inline-formula> ) of 0.94345 and a Pearson’s correlation coefficient of 0.9725 between the actual model and the model of the proposed approach, versus 0.9681 and 0.9716 for the best single and popular ensemble models, respectively. Further, we tested the effectiveness of our method using real seismic data from the North Sea. With a Pearson’s correlation value of 0.9743, the inverted model ranges from 27 to 35%, compared to the reference model, which has an overall range of 20 to 33%. These results provide insights into the potential of the proposed method and its applicability to any other seismic volume to determine spatially varying underground porosity from seismic attributes directly.

Negation Invariant Representations of 3-D Vectors for Deep Learning Models Applied to Fault Geometry Mapping in 3-D Seismic Reflection Data

We can represent the orientation of a plane in 3D by its normal vector. However, every plane has two normal vectors that are negatives of each other. We propose four novel representations of vectors in 3D that are negation invariant and can be used by a neural network to predict orientation. Our proposed solution is the first to introduce representations that are negation invariant, continuous and easily parallelisable on the GPU. We evaluate the representations by predicting the orientation of a plane on a toy task, and by applying them to synthetic seismic tomographic data where we predict the presence and orientation of faults for every voxel in the volume. We further make use of the orientation of the faults in a post-processing algorithm on the GPU that separates the faults into segments (i.e. instances) that do not intersect, which allows us to selectively visualise faults in 3D. We demonstrate the utility of the representations by deploying the model on the Laminaria 3D Seismic volume as a case study. We quantitatively compare the model’s prediction against human interpretations of slices through the volume as well as existing interpretations in literature. Our analysis shows good agreement (F1 score of 88%) of the model with human interpretation in the shallow levels, where the ambient noise is lower, but this agreement degrades at deeper levels (F1 score of 68%). We explore possible reasons for this degradation.

Intelligent seismic stratigraphic modeling using temporal convolutional network

Deep learning (DL) has stimulated the intelligent processing and interpretation of seismic big data for oil and gas exploration in the past decade. In the applications to seismic stratigraphic modeling, most DL algorithms utilize a combination of ‘convolution + pooling’ to extract spatial features from seismic volume or images, which, however, may lead to a loss of the fine-grained information endowed in seismic time-series records inevitably. Here, we proposed a novel scheme for intelligent seismic stratigraphic interpretation based on temporal convolution network (TCN). The purpose of using TCN is to implement a trace-by-trace segmentation of lithologies by extracting spatio-temporal correlation of seismic record from a small amount of well log labels or manually interpreted geological profiles, thereby building a 3-D subsurface stratigraphic model. The proposed TCN model for seismic stratigraphic interpretation is trained, validated, and tested using the Netherlands F3 seismic dataset. The results suggest that, with a limited number of either manual interpretation profiles or well logs information, the proposed TCN algorithm can produce a highly accurate and laterally continuous 3-D stratigraphic model. Specifically, the mean pixel accuracy of prediction results of the above two schemes (compared to the real model) has achieved 98.23% and 96.09%, respectively. In addition, comparing TCN to the widely used encoder-decoder convolutional neural networks for seismic strata interpretation shows the outperformance of TCN scheme in terms of training time, model size, and prediction accuracy, while also revealing that the proposed TCN model can well identify thin strata in complex seismic data.

Deep learning for characterizing CO2 migration in time-lapse seismic images

Time-lapse (or 4-D) seismic data play an important role in monitoring the spatial CO2 distribution during and after the injection period. However, traditional interpretation or prediction of CO2 distribution is time-consuming and might be sensitive to the quality of 4D seismic data. To solve these problems, we propose a deep-learning-based method to efficiently and accurately characterize CO2 plumes in time-lapse seismic data. We first introduce a workflow to build 3-D realistic impedance models containing CO2 plumes with various shapes, sizes, and locations. From the impedance models, we then simulate synthetic seismic datasets and automatically obtain the corresponding CO2 label volumes. We extract real noise from field seismic datasets and add the noise to the synthetic ones to make them more realistic. We further construct a diverse and realistic training dataset with the combination of synthetic data containing CO2 plumes and real data without CO2 plumes that are randomly cropped from field seismic data before CO2 injection. We finally utilize the training datasets without any human labeling to train a 3-D deep U-shape convolutional neural network for detecting CO2 plumes in the Sleipner time-lapse seismic images. Compared to traditional interpretation methods that take several days or even weeks, our method takes only 29 s using one graphics processing unit (GPU) to predict CO2 plumes in a 512*512*256 seismic volume. Besides, our CO2 prediction can achieve 95.8% accuracy (compared to the manual interpretation) and could distinguish reflections of CO2 plumes from the ones of pre-existing fluids, thin layers, and noise. To more accurately characterize the CO2 plumes migration, we use dynamic image warping to compute relative shifts that register the time-lapse seismic volumes before and after CO2 injection and then apply the same shifts to the predicted CO2 plumes. By doing this, we are able to reduce the inconsistencies that may be introduced by acquisition, processing, push-down effect (velocity decrease by injected CO2), and pull-up effect (wavelet distortion), which is helpful to more accurately characterize the CO2 plumes migration.

Simultaneous Sparse Deconvolution

Increasing seismic resolution has been long pursued by the geophysical community, serving, among other applications, for more detailed interpretation of seismic clinoforms, better seismic inversion and quantitative interpretation. Sparse deconvolution methods play central role in this pursuit. However, although sparse methods have performed well for single seismic stack or acoustic inversion tasks, their application for multi-stack seismic volumes and consequent use in elastic inversion is still a challenging and ongoing research topic. The challenge is to obtain reflectivity volumes with high correlation between them. In this study, we present a new method to perform simultaneous sparse deconvolution (SSD) in a group of seismic volumes associated with different reflection angles. The proposed algorithm enforces co-localization of the spikes on the estimated reflectivity traces and additionally allows user control of the sparsity via hyperparameters. The method is validated in both synthetic and real datasets proving its co-localization capability and resulting in higher correlations between the reflectivity volumes, when compared to independent sparse deconvolution (ISD) of the seismic stacks. The resulting reflectivity volumes are, henceforth, better suited for downstream tasks such as high resolution amplitude versus angle (AVA) analysis, or input for high resolution elastic inversions.

Seismic multi-attribute approach using visual saliency for subtle fault visualization

This study improves a collection of attributes to detect subtle faults in three dimensional data obtained from the Krishna-Godavari (KG) basin, with results displayed on synthetic and real datasets. Seismic attributes, for instance, curvature and coherence, are often used to delineate discontinuities, such as faults and fractures where hydrocarbons may have been trapped. These attributes have their advantages subjective to the seismic data. In this paper, we propose a multi-attribute framework for identifying subtle faults inside seismic volumes. Curvature attribute is a powerful and popular technique to deal with these faults. The faulted horizon is fitted on the quadratic surface using the least-square method, and the most positive and most-negative curvature attributes are calculated, which are further used in saliency map calculations. Several signal processing techniques, such as Hough transform and ant tracking, have been used to delineate faults. Here, we have proposed a novel signal processing approach based on energy variations known as top-down saliency on the curvature attributes using 3D-FFT local spectra and multi-dimensional plane projections. To analyze the directional nature of seismic data, the directional center-surround technique is employed for visual attention. Furthermore, the log-Gabor filter and image erosion are applied to the saliency-rendered seismic volume to highlight the oriented amplitude discontinuities at faults. Most of the time, these discontinuities may not be very prominent to find the subtle faults and other trace-to-trace hidden geological features in three-dimensional seismic data. In our work, calculated attributes assist us in mapping these changes, because they are all differently sensitive to the faults and fractures in unique ways. Experimental results on real field seismic data from the Krishna-Godavari basin prove that the proposed algorithm is effective and efficient in tracking subtle and minor faults, better than previous works.

3D generalized spherical multifocusing seismic imaging

We introduce a 3D generalized spherical multifocusing (GSMF) algorithm to generate a high-resolution 3D stacked volume that is equivalent to a synthesized 3D zero-offset wavefield for crooked-line/3D seismic data. The proposed algorithm can be applied to arbitrary recording geometry from areas with irregular topography, a complex near the surface, and complex subsurface. The 3D GSMF method simultaneously corrects for elevation statics, nonhyperbolic moveout associated with reflections beneath complex overburden structures, and azimuth-dependent dip-moveout effects. In addition, the formulation is dually generalized for the optical domain and the effective medium. The optical domain and effective medium parameterizations account for heterogeneity either by shifting the reference time to project the problem into the optical image space or by adjusting the velocity of an effective overburden, respectively. We test the performance of our method using 3D synthetic data with 3D and crooked-line surveys. The numerical tests have shown that the accuracy of the new approximation is significant for gently to highly curved interfaces beneath low to relatively high heterogeneous overburden with rugged topography, even at large offsets and midpoint separations. In addition, we rigorously evaluate the method using 3D real seismic data acquired over a complex thrust-belt area with rugged terrain. Compared with conventional 3D stacking, the new formulation yields a high resolution and accurate seismic stacked volume from land seismic data collected with arbitrary 3D geometries.

The geometry of the Western Boundary Fault (WBF) of Amer oil field and its influence on hydrocarbon trapping, western central Gulf of Suez, Egypt

Amer ridge holds one of the most prolific oil fields in the western central Gulf of Suez rift basin and was discovered in 1965. The brownfield produces oil from the Miocene-Eocene carbonates and Late Cretaceous sandstone reservoirs with oil water contact 1150 m. The present work provides a structural model expressing the role of inherited structures and the tectonic evolution of the western fault bounding the field. This model resulted from the multi-scale integration of subsurface data from 90 wells, various borehole images, and 3-D seismic volumes attributes. The interpretation demonstrated the vertical profile of the western boundary fault of Amer field (WBF) to be a ramp-flat-ramp geometry pattern cutting through lithological units of different rheology. The developed fault surface of the WBF divided the penetrated sedimentary succession into two distinct strain intervals. The inherited structural fabrics of the lower interval of pre-rift units are documented with 1) an anomalous southwest-stratal dip, and 2) severe deformation of the WBF footwall with extensional faults of gulf parallel and gulf-orthogonal trends. Simultaneously, the roll-over anticline-syncline structure that developed within the upper, less-strained Miocene interval, coincided with the flat of the WBF geometry.By applying this model, it was possible to i) distinguish the three-footwall structurally controlled plays of Amer area into lower ramp-1, flat, and upper ramp-2 and ii) enlarge the size of the brownfield. Recent development activity in Amer area verified the new structural model and expanded the drilling activity to target the Paleozoic clastic reservoir of Nubia C as a new exploration objective. The Amer structural model can serve as a useful analogue for oil field development and production enhancement in other complex rift basin settings.

Using active source seismology to image the Palos Verdes Fault damage zone as a function of distance, depth, and geology

Fault damage zones provide a window into the non-elastic processes of an earthquake. Geological and seismic tomography methods have been unable to measure damage zones at depth with sufficient spatial sampling to evaluate the relative influence of depth, distance, and lithological variations. Here, we identify and analyze the damage zone of the Palos Verdes Fault offshore southern California using two 3D seismic reflection datasets. We apply a novel algorithm to identify discontinuities attributed to faults and fractures in large seismic volumes and examine the spatial distribution of fault damage in sedimentary rock surrounding the Palos Verdes Fault. Our results show that damage through fracturing is most concentrated around mapped faults and decays exponentially to a distance of ∼2 km, where fracturing reaches a clearly defined and relatively undamaged background for all examined depths and lithologies (450 m to 2.2 km). This decrease in fracturing with distance from the central fault strand exhibits similar functional form to outcrop studies. However, here we extend analysis to distances seldom accessible (∼10 km lateral distance). Separating the data by geologic units we find that the damage decay and background level differs for each unit, with the older and deeper units having higher levels of background fracturing and shallower exponential decays of fracturing with distance from the fault. Surprisingly, these differences in damage decay and background level trade-off result in a consistent damage zone width regardless of lithology or depth. We find that the damage zone has similar decay trends on both sides of the fault. When examining the damage zone at shorter (4 km vs 17 km) along strike distances, the damage zone has a more complex decay trend and at least two strands are resolvable.

Oil Reservoir Detection Using Volume Attributes in Chia Surkh Area, Kurdistan Region, Iraq

This study includes picking seven reflectors from intense sampling of 3D Seismic survey composed of 40 inlines and 30 crosslines, the distance between the seismic lines is equal to 250m. The study deals with extracting and analysing volume seismic attributes of 3D seismic data of Chia Surkh area. The area is located east of Kalar City, Sulaimani Government, Kurdistan Region, Iraq. The main aim is to determine locations of hydrocarbon accumulation by picking seven horizons as well as studying and analysing both subsurface structure and direct hydrocarbon indicators. The seismic volume attributes are calculated from the original seismic data that consist of (400) inlines and (300) crosslines, and the distance between two adjacent lines is equal to (25m). The seismic attributes used in the current study are, variance edge, curvature, root mean square, envelope and sweetness. Revealed existence of a major anticline, a major fault. As well as two small faults were detected and running parallel to the major fault, while the envelope and sweetness attributes emphasized appearance of several bright spots within Lower Fars, Dhiban, Jeribe and Euphrates Formations, which give quite evidence for accumulation of hydrocarbon. The study concluded that the complete system requires for existence of petroleum is available such as the major anticlinal structure, carbonate rocks as an excellent reservoir rocks and gypsum or (anhydrite) as an optimal cap rock. The existence of hydrocarbon is confirmed by bright spots.

Structural smoothing on mixed instantaneous phase energy for automatic fault and horizon picking: case study on F3 North Sea

Today, with the development of 3-D studies and the increase in seismic data volume, there is a growing need to expand interpretation techniques for achieving higher speed and accuracy of interpretation tasks. Determining seismic faults and horizons is vital to accomplish the process as one of the essential stages of data interpretation. With the recent development of computational methods in seismic interpretation and their benefits, different approaches have been promoted. The specialist can make the understanding much faster with higher accuracy. In this research, a fully automated dual horizon and fault selection approach in the presence of semi-vertical faults is presented using a structural smoothing condition. Geological faults make it challenging to map sedimentary layers appropriately which is targeted for review in this work. Unlike Image processing techniques that determine the location of faults only, the proposed approach gives the benefit of the estimated fault displacement. In this method, faults are modeled as a displacement vector field. Despite traditional methods (such as similarity and coherence), in this method, the vector field of the estimated fault displacement determines the displacement and its location. This vector field can be used for auto-determination of fault-related layers displacement. As a result, automatic horizon picking in the presence of such faults is possible, thereby simplifying the mapping of sedimentary layers.

Four-Dimension Seismic Analysis in Carbonate: A Closed Loop Study

Four-dimensional seismic analysis is an effective reservoir surveillance tool to track the changes of fluid and pressure phases in the oil and gas reservoirs over time of the baseline and monitoring seismic acquisition. In practice, the 4D seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. Therefore, 4D seismic analysis is a model for integrating various disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. In this study, we started the 4D seismic workflow with a 1D well-based 4D feasibility study to detect the likelihood of 4D signals before performing 4D seismic co-processing of the baseline and monitoring surveys starting from the seismic field data of both datasets. As part of a full 4D seismic co-processing of the baseline and monitor surveys, 4D seismic metric attributes were analyzed over the survey area to measure the improvement in seismic acquisition repeatability for the scarce 1994 baseline seismic and the 2014 monitor seismic survey. For the monitor survey, a 4D time-trace shift was performed using the baseline survey as a reference to measure the time shifts between the baseline and monitor surveys at 20-year intervals. The 4DFour-dimensional dynamic trace warping was followed by a 4D seismic inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contaminations, and differences in dynamic amplitude range between the baseline and monitor seismic surveys. We then examined the relationship between well logs and seismic volumes by predicting a volume of log properties at the well locations of the seismic volume. In this method, we computed a possibly nonlinear operator that can predict well logs based on the properties of adjacent seismic data. We then tested a Deep Feed Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using the baseline and monitoring seismic inverted data. The objective of trying such a deep machine learning approach was to predict the density and porosity of both the baseline and the monitoring seismic data to validate the accuracy of the 4D seismic inversion and evaluate the changes in reservoir properties over a time-lapse of 20 years of production from 1994 to 2014. Finally, we matched the 4D seismic signal with changes in reservoir production properties, investigating the mechanism underlying the observed 4D signal. It was found that the detectability of 4D signals is primarily related to changes in fluid saturation and pressure changes in the reservoir, which increased from 1994 to 2014. This innovative closed-loop research proved that the low repeatability of seismic acquisition can be compensated by optimal 4D seismic co-processing with a complete integration workflow to assess the reliability of the 4D seismic observed signal.

Porosity estimation by neural networks for CO2 storage in Otway site

Dynamic simulation of CO2 migration requires a variety of modeling parameters fed by geomechanical models. The confidence of these parameters of material groups such as porosity and permeability is crucial in achieving successful simulations. Based on the geomechanical and geophysical parameters, we estimated porosity distributions on the Paaratte Formation in the Otway site, one of the CO2 storage project in Australia. Considering the nonlinear relations between porosity logs and seismic data, we applied the neural network scheme that addresses the porosity value across a whole domain. With only one monitoring well and two injection wells at the site, seismic data are used to restore the spatial absence in porosity. The technique of the neural network was conducted based on the integration of the well logs to the seismic volume and the inversion of acoustic impedance. The results indicated that a correlation value of the well and the seismic tie is 75% and the value between the recorded and the estimated porosity is 87% on average. Further, the time slice maps of porosity at a depth of the injection interval demonstrated a CO2 plume developed in the Paaratte formation of the Otway site.

Fold formation in Kurdish Zagros (N Iraq)

Analysis of seismic sections and wells near the frontal folds of Iraqi Kurdistan (Low- and Highly folded zone) document the interplay of folds, faults and possible mechanisms of their generation.Serial sections from 3D seismic volume in the Low folded zone are interpreted based on calibration wells. Sections display fold evolution from low amplitude kinked monocline to high amplitude ramp folding. The fold continues towards the exposed Safeen anticline with a subvertical limb. These lowland sections can also be considered as the lateral growth segment of Safeen anticline.Three geoseismic sections through frontal folds were constructed with variable quality seismic and dips, formation tops from surface and from mountains wells. These sections show tight anticlines with thrust faults cutting to near surface on both limbs; remnant normal faults; a subvertical southern limb crossed by flat thrusts; and a Triassic ductile core.Sections suggest that this part of Zagros is built of two ‘rigid’ units: an upper, Mesozoic-Neogene and a lower, Paleozoic one, separated by a ductile detachment along the lower Triassic. The upper unit is prone to folding and faulting, the lower one to faulting. The detachment in-between comprises the Kurrachine C, lower Triassic Beduh and Mirga Mir formations, and potentially the top members of Permian Chia Zairi formation. Detachment is possibly dominated by evaporites. Our detachment is thicker than only Beduh-Mirga Mir detachment proposed by recent reconstructions of others.Folds are initiated as detachment folds above thickened parts of the Lower Triassic, then evolve into ramp folds. Ramp is formed by the merger of flat thrusts with pre-existing steep earlier normal faults that affect the Mesozoic section. Shear and drag beneath these ramps lead to steepening of the southern limb and to fault propagation. A basal flat thrust is the final element of this fault propagation stage. Underlying rigid Paleozoic can form imbricates that push the Triassic detachment upwards, creating passive folds. These Paleozoic imbricates dip to the south and not to the north, as suggested by reconstructions of others. Finally, strike slip shear documented at outcrops also contributes to the final fold form.Own and others' surface geological observations, indications from seismic and radiometric data suggest that fold generation started in Late Cretaceous, continued in the Paleogene in internal Highly folded zone. In our study area only Late Neogene fold growth can be documented, which is in harmony with 5–8 Ma uplift dates of others. Others’ geomorphologic work suggests fold lateral growth is a quick process. We propose that regional folds, such as Safeen anticline grow in distinct short periods separated by longer quiet intervals.

Cenozoic evolution of mobile shales and fluid migration to seafloor: 3D seismic evidence from the offshore western Niger Delta

The offshore Niger Delta provides a spectacular example of gravity collapse tectonics, but the timing of shale mobilisation remains poorly understood. Here we present new information from the western Niger Delta, based on a detailed interpretation of a 3D seismic volume, calibrated with biostratigraphic data from exploration wells. The study area is underlain by mobile shales containing thrust-fold anticlines, overlain by >5 km thick Late Eocene to present succession of folded and faulted sediments with fluid migration features. Sedimentation rates estimated at one deep well increased during two phases, more than doubling during the Late Eocene to Serravallian (39.5–12.5 Ma), and by up to ten times during the Tortonian (9.5 Ma) to present. Thinning and onlapping geometries within the lowermost, Late Eocene-Burdigalian (39.5–18.5 Ma) interval are interpreted to record syn-depositional deformation. This indicates that shale tectonics in the offshore western Niger Delta initiated early in the evolution of the region, supporting previous interpretations that the active compressional zone of the Niger Delta was in the present continental slope before prograding to the outer-fold-thrust belt in the Pliocene. Stratal thinning above the crests of thrust-fold anticlines in the northeast and southwest of the study area suggests that shale tectonics persisted throughout the Neogene and Quaternary. A chaotic column that rises to seafloor from the crest of thrust-fold anticline, interfingering with the stratified succession is interpreted as a mud volcano edifice that has been active since at least the Burdigalian. This supports early shale mobilisation in response to generation of overpressure and also the fact that mud volcano system once formed, may act as conduits for pressure release throughout the history of a gravity-driven collapse system. The spatial association of seafloor mud volcanoes and pockmarks with deeper thrust-fold anticlines and normal faults suggests fluid migration from depth. In contrast, pockmarks overlying mass-transport deposits and submarine channels suggest fluid-flow from dewatering of more recent sediments. Our findings provide new insights into the Cenozoic tectono-stratigraphic evolution of the offshore western Niger Delta and the relation of shale mobility to fluid migration and escape during continental margin collapse.

Faults and fractures detection using a combination of seismic attributes by the MLP and UVQ artificial neural networks in an Iranian oilfield

Faults and fractures play a significant role in oilfield drilling operations, hydrocarbon trapping, and reservoir development. Exploring faults quickly and accurately can help reach the target more efficiently. In this study, applicable seismic attributes were combined using a multilayer perceptron and an unsupervised vector quantizer and applied to a 3D seismic cube to identify discontinuities. First, high-probability faulted areas were picked manually on a seismic section as an input pattern for the MLP. Then, particular seismic attributes (dip-steering, similarity, coherency, curvature, instantaneous) were applied to the data. Consequently, the MLP and UVQ were used to determine the most contributed attributes. Using the MLP and UVQ, the most relevant attributes were integrated to find faults and fractures in the 3D seismic volume. In contrast to some fault-identifying methods and prior studies, this study used not just steered attributes but also compared supervised and unsupervised neural networks. Eventually, comparing the outputs of the MLP, faulted and non-faulted cubes, with the initial seismic section and the UVQ’s output revealed discrepancies. For a specific set of attributes, the MLP was obviously superior to the UVQ in terms of creating detailed outputs, analyzing time, and rendering more precise and trustworthy results.