Seismic Attributes Research Articles

Seismic driven reservoir classification using advanced machine learning algorithms: A case study from the Lower Ranikot/Khadro sandstone gas reservoir, Kirthar Fold Belt, Lower Indus Basin, Pakistan

Reservoir characterization of thin sand bed reservoirs has been a challenge for petroleum explorers across the globe. In this study, we have studied the heterogeneous Paleocene sandstone gas reservoir of Lower Ranikot/Khadro, Kirthar Fold Belt, Lower Indus Basin, Pakistan. The studied reservoir is par below seismic resolution, with an average thickness that varies from 4 to 7 m at places and has provided a good amount of prosecution in some producing wells. The solution to the above challenges has been put forth where an integrated approach of seismic attributes, petrophysical properties, well logs, and facies data sets are combined with advanced machine learning algorithms to get better resolution for the studied thin-bedded sandstone resource. In addition, the thin heterogeneous sands of the Ranikot/Khadro Formation were characterized by predicting the elastic properties of the reservoir and classifying the facies distribution of hydrocarbon-bearing sands within the Zamzama Gas Field. Several machine learning (ML) algorithms were chosen and implemented on pre-stack seismic data from seven wells, two of which were produced from the Paleocene reservoir. The Gradient Boosting Regressor (GBR) produced promising results with the demarcation of thin sands with high accuracy levels. Based on a successful petro-elastic relationship, thin gas sands were highlighted. The low VCL of 30% and Sw of 45% cut-off reflect gas sands with P-impedances of 7500–9500 m/s × g/cc and a Vp/Vs ratio of 1.4–1.6 as decisive ML characteristics. Based on the applied workflow aided by ML, three reservoir units have been assigned for the Paleocene hydrocarbon-bearing sands, and gas sand probability distribution maps have been developed for each reservoir unit. Aligning the seismic inversion technique with machine learning advanced algorithms not only speeds up the prediction of elastic properties for facies classification but also increases accuracy to over 90% while drastically reducing uncertainty and associated costs in the process. Advanced machine learning algorithms have allowed us to cater to thin sand beds by eliminating the need for injecting frequencies. Instead, it recognizes features from the well data, trains itself, and predicts the corresponding features on the seismic trace, thus effectively allowing us to characterize the beds that are below seismic resolution in any given complex geology. The generated probability distribution maps of the three recognized thin-bedded sand units can be the basis for petroleum exploration and development in the study area. The discussed technique is successfully applied to complex folds and thrust belts, and hence can be implemented successfully in other heterogeneous thin-bed reservoirs for the successful classification of hydrocarbon-bearing sand distribution. It would optimize the results in a short period of time and with fewer economics.

Pore and Microfracture Characterization in Tight Gas Sandstone Reservoirs with a New Rock-Physics-Based Seismic Attribute

Pores and microfractures provide storage spaces and migration pathways for gas accumulation in tight sandstones with low porosity and permeability, acting as one of the controlling factors of gas production. The development of a rational rock physics model is essential for better understanding the elastic responses of tight sandstone with complex pore structures. Accordingly, seismic characterization of pores and microfractures based on the rock physics model provides valuable information in predicting high-quality tight gas sandstone reservoirs. This paper proposes a rock-physics-based approach to compute the pore–microfracture indicator (PMI) from elastic properties for pore structure evaluation in tight sandstones. The PMI is achieved based on the axis rotation of the elastic parameter space using well-log data. The rotation angle is determined by finding the maximum correlation between the linearized combination of the elastic parameters and the introduced factor associated with total porosity and microfracture porosity. The microfracture porosity is then estimated with an inversion scheme based on the double-porosity model. Finally, the optimized rotation angle is employed to compute the PMI with seismic data. The obtained results are of great benefit in predicting the permeable zones, providing valuable information for sweet spot characterization in tight gas sandstone reservoirs.

Self-Supervised Learning for Seismic Data: Enhancing Model Interpretability With Seismic Attributes

Deep learning has shown great potential in geosciences, such as seismic data processing and interpretation, improving decision-making and reducing analysis time. However, deep learning faces two main challenges. First, many deep learning models rely on labeled data, which can be time-consuming to obtain. Second, the predictions from these models often lack interpretability, making it difficult to use them for high-value decisions. To address these limitations, we propose a novel workflow that eliminates the need for labeled data and enables interpretation of the results, highlighting key geological features. The proposed workflow trains a Vision Transformer (ViT) to produce six attention maps, focusing on diverse and relevant regions, by assigning higher attention values. We first train the ViT using a modified DINO (Distillation with No Labels) method specifically designed for the seismic domain and monitor for overfitting. Then, to evaluate the focus of each attention head, we use nine seismic attributes as predictor features for the assigned attention using a Gradient Boosting Model. Finally, the method samples the seismic attributes in stationary regions of the attention maps and calculates SHAP values to determine the most impactful attributes on the attention prediction. Each attention head can concentrate on unique geological features of the input seismic image, as indicated by the different relationships between SHAP values and seismic attributes. Additionally, regardless of location, each attention head can detect the same geologically significant pattern based on the attributes used. The proposed workflow enables the interpretability of the model’s importance, guided by expert knowledge through seismic attributes.

Identification of Hydrocarbon Zones and Chimneys Using Seismic Attributes Analysis at Woda Oil Field, Western Offshore Niger Delta

This research investigated hydrocarbon zones and chimneys using seismic attributes analysis in Woda Oil Field, Western offshore Niger Delta. Seismic attributes are powerful tools in monitoring and predicting hydrocarbon zones. Detailed application of seismic attributes on the Woda seismic data aided in the extraction of important information about the geologic features on seismic profiles. Materials used in this research include well logs, seismic data and 3D Post Stack Depth Migration (PSDM) seismic volume. Methods used were seismic attribute analysis and well log interpretation. The reservoirs of interest were identified and analyzed using well log data based on gamma ray, resistivity, and Vp sonic log response. Seismic attributes such as chaos, root mean square velocity, similarity and reflection intensity were quality controlled, enhanced, and generated which assisted in identifying chimneys and hydrocarbon zones. The majority of the chimneys in this research may have nucleated in the lower part of the faulting system below the partially permeable portion of the fault planes and across a regional partially permeable barrier as revealed by thin fault likelihood attribute. Gas expulsion along the lower portion of the complex faulting system perhaps, may have preconfigured the spatial organization of the chimneys. Pockmarks formation above the chimneys as revealed by the sweetness attribute may have inherited the same linear planiform geometry, and both structures recorded the timing of hydrocarbon zones and gas expulsion within the Woda Oil Field.

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.

Integration of Rock Physics and Seismic Attributes for Reservoir Quality Assessment of ‘Solid’ Field, Offshore Niger Delta

The decline in the chance of finding commercial quantity of hydrocarbon necessitated the integration of more geophysical tools in order to properly assess the reservoir quality. This study integrated rock physics and seismic attribute to assess the reservoir quality in ‘Solid’ field, offshore Niger Delta. The objectives of this study are to use rock physics analysis to quality check the seismic attribute results and to locate the best productive zones in the field. 3D seismic data and well log data for six wells were used for Formation evaluation, Petrophysical analysis, Structural analysis, Seismic attribute extraction. These results were then checked qualitatively using rock physics analysis. Six sand units (Sand 1, 2, 3, 4, 5 and 6) were correlated across six wells. Petrophysical analysis indicated that the reservoirs have high porosity (20.9% to 25.4%), high permeability (1897 to 2557 mD), high net-to-gross sand (84.7% to 99.0%), and low volume of shale (0.13 to 0.48). This shows that the reservoirs are of good quality. The depth structure map indicates that the reservoirs are mainly fault assisted closures. The seismic attribute extraction for the sand reservoirs shows high amplitude for Sand 1 in areas where wells have been drilled and also in some closures in Sand 2, 3 and 4 which is indicative of hydrocarbon presence. The crossplot of acoustic impedance against shear impedance of Well S5 helped to differentiate sand and shale in the reservoirs. Crossplot of acoustic impedance against Vp/Vs ratio was then used to predict the fluid type in Well S5. The crossplot of acoustic impedance against Vp/Vs ratio of Sand 2 in Well S5 and Sand 1 in Well S4 follows a regular trend with depth. The overall results portrayed that Sand 1, 2, 3 and 4 are of good quality and are oil bearing.

Applying the most frequent values assisted Hilbert transform into seismic attributes

In the present study, we developed a new method to calculate two of the most important seismic attributes that can able to give a huge impact in the interpretation field leading to a better geological and geophysical interpretation of the data. The first attribute was the Reflection Intensity with great improvement in detection of thin layers that show low reflection coefficient and enhance the ability to follow unconformity, the other one is the Instantaneous Phase to discover the discontinuity of events. The new method depends on combining the most frequent values (MFV) with inversion to calculate the Hilbert transform and applying hybrid Seismic Denoising, using also dual inversion and calculated Steiner weight from the iterative reweighted least square method (IRLS). Scaled Hermite function in a series expansion was used to discretize the spectrum of the time trace. The procedure followed with adjusting the process using multi-windows technical on the generated synthetic seismic traces loaded with Gaussian and Cauchy noise at the same time. The result of the method was shown significant effects on outliers or normal Gaussian noise when applied to two seismic attributes better than the old way using the Discrete Fourier Transform (DFT) method.

Explainable Convolutional Neural Networks Driven Knowledge Mining for Seismic Facies Classification

Seismic facies analysis is a crucial foundation for basin-fill studies and oil and gas exploration. With its rapid development, CNN-assisted interpretation is becoming increasingly popular. However, CNN models are often considered "black boxes" that lack transparency. To understand how CNN models classify seismic facies and visualize the contribution of each seismic attribute to the final predictive scoring, we have investigated class activation map (CAM) techniques and an explainable tool called Shapley additive explanations (SHAP) value. Based on real seismic data collected in the Sichuan basin, we compared the visualization performances of CAM and SHAP methods and found that the SHAP tool has better visualization capabilities than CAM methods, which only produce heat maps with positive values. Using SHAP values, we identified the importance of each seismic attribute and refined redundant attributes. This approach establishes a connection between seismic attributes and sedimentary environments and is a prime example of the capability of deep learning to discover knowledge beyond human experience. We applied the selected seismic attributes to generate a refined CNN model and compared it to the original CNN model, demonstrating the superiority of our proposed strategy. When we compared the predicted seismic facies using the refined CNN model based on SHAP features, the conventional K-means, SVM and Gaussian Naive Bayes methods, it is observed that our predicted map aligns well with geological knowledge with less prediction errors, demonstrating the effectiveness and feasibility of our developed strategy.

Seismic Attributes for Enhanced Structural and Stratigraphic Interpretation in Onshore Fuba field, Niger Delta, Nigeria

Seismic attributes for enhanced structural and stratigraphic interpretation of Fuba Field, Onshore Niger Delta, Nigeria using Well-log and 3D Seismic data are here presented. Well-to-Seismic ties, faults and horizon mapping, time-surface generation, depth conversion and seismic attributes generation were carried out using Petrel software. The structural interpretation of seismic data reveal highly synthetic and antithetic faults which are in line with faults trends identified in the Niger Delta. Of the 29 interpreted faults, only synthetic and antithetic faults are regional, running from the top to bottom across the field. These faults play significant roles in trap formation at the upper, middle and lower sections of the field. Two distinct horizons were mapped. Fault and horizon interpretation reveal closures which are collapsed crestal structures bounded by these two major faults. The depth structure maps reveal anticlinal faults. Reservoirs are found at a shallower depth from 6500 to 7500ft and at a deeper depth ranging from 11500 to 13000ft. The synthetic and antithetic faults act as good traps for the hydrocarbon accumulation in the study area. The variance and chaos values range from 0.0 to 1.0. The dip magnitude and azimuth values range from 0 to 90 degrees and 0 to 360 degrees. The variance edge, chaos, dip magnitude and azimuth analysis were used to delineate the prominent and subtle faults in the area. The results of spectral decomposition at a frequency of 12-35Hz indicates areas of low frequency and high amplitude associated with known hydrocarbon zones, the presence of channels, lobes and small scale faults in the field. The result of the seismic attribute analysis has shown that the Fuba field has good hydrocarbon prospects.

Application of 3-D Seismic Attributes Analysis for Hydrocarbon Prospectivity in Onshore Fuba field, Niger Delta, Nigeria

Application of 3-D seismic attributes analysis for hydrocarbon prospectivity in the onshore Fuba Field, Niger Delta, Nigeria using Well-logs and 3D Seismic data are here presented. Well-to-seismic ties, faults and horizon mapping, time-surface generation, depth conversion and seismic attributes generation were carried out using Petrel software. The structural interpretation of seismic data reveal highly synthetic and antithetic faults which are in line with faults trends identified in the Niger Delta. Of the 36 interpreted faults, only synthetic and antithetic faults are regional, running from the top to bottom across the field. These faults play significant roles in trap formation at the upper, middle and lower sections of the field. Three distinct horizons were mapped. Reservoir M is found at a shallower depth from 10937 to 10997 ft, reservoir N is found at a depth ranging from 11213 to 11241 ft while reservoir O is found at a deeper depth ranging from 11681 to 11871 ft respectively. Analysis of the relevant seismic attributes such as variance edge, root mean square, maximum amplitude, average magnitude and maximum magnitude were applied to the seismic data. The variance values ranges from 0.0 to 1.0. The Variance edge analysis was used to delineate the prominent and subtle faults in the area. The RMS amplitude values ranges from 9,000 to 13,000 in the reservoirs. The root mean square amplitude, maximum amplitude, average magnitude and maximum magnitude analysis reveal bright spot anomaly. These amplitude anomalies served as direct hydrocarbon indicators (DHIs), unravelling the presence and possible hydrocarbon prospective zones. Results from this study have shown that away from the currently producing zone of the field, additional leads and prospects exist, which could be further evaluated for hydrocarbon production

Seismic Attributes Analysis as a Precursor for Hydrocarbon Indication: A Case Study of Onshore Fuba field, Niger Delta, Nigeria

Seismic attributes analysis as a precursor for hydrocarbon indication in onshore Fuba Field, Niger Delta, Nigeria using Well-logs and 3D Seismic data are here presented. Well-to-seismic ties, faults and horizon mapping, time-surface generation, depth conversion and seismic attributes generation were carried out using Petrel software. The structural interpretation of seismic data reveal highly synthetic and antithetic faults which are in line with faults trends identified in the Niger Delta. Of the 36 interpreted faults, only synthetic and antithetic faults are regional, running from the top to bottom across the field. These faults play significant roles in trap formation at the upper, middle and lower sections of the field. Three distinct horizons were mapped. Reservoir M is found at a shallower depth from 10937 to 10997 ft, reservoir N is found at a depth ranging from 11213 to 11241 ft while reservoir O is found at a deeper depth ranging from 11681 to 11871 ft respectively. Analysis of the relevant seismic attributes such as variance edge, root mean square, maximum amplitude and relative acoustic impedance were applied to the seismic data. The variance values ranges from 0.0 to 1.0. The Variance edge analysis was used to delineate the prominent and subtle faults in the area. The RMS amplitude values ranges from 9,000 to 13,000 in the reservoirs. The root mean square amplitude and maximum amplitude analysis reveal bright spot anomaly. These amplitude anomalies served as direct hydrocarbon indicators (DHIs), unravelling the presence and possible hydrocarbon prospective zones. The relative acoustic impedance analysis was used for delineating lithology variation in the seismic sections. Results from this study have shown that away from the currently producing zone of the field, additional leads and prospects exist, which could be further evaluated for hydrocarbon production.

3-D Structure-Oriented Adaptive Gaussian Pyramid for Seismic Multiscale Fracture Detection

Gaussian pyramid (GP) has the property of decomposing the 2D data into multiple scales. It has been successfully applied in image processing, but rarely used in seismic data processing and interpretation. Specifically, seismic data is not just a 2D image in one time slice or horizon. Moreover, the pattern of geological body varies rapidly in 3D spaces. Hence, the classical GP method exists a limitation in processing 3D seismic data. Especially in fracture detection, the 2D isotropic Gaussian kernel used in GP tends to blur the fracture details. In this letter, to match the high dimensional seismic data, we propose a structure-oriented adaptive Gaussian pyramid (SOA-GP) algorithm, which expand the 2D GP method to 3D situation. In this case, to eliminate the influence of transverse change of wave impedance and consider stratigraphic characteristics, the 2D Gaussian filter is also substituted by the 3D structure adaptive anisotropic Gaussian kernel, which is constructed by instantaneous phase-based gradient structure tensor (GST) method. Meanwhile, seismic attributes are applied to the multi-resolution seismic data decomposed by SOA-GP method to realize multi-scale fracture detection. Finally, we apply this new 3D SOA-GP method to a field data. It demonstrates that the multi-scale decomposition of the new method could produce more details of fracture and less disturbs from noise.

Analysis of a Pull-Apart Basin and Its Associated Fractures in the Woodford Shale, Central Oklahoma

Pull-apart basins are faulting and folding zones with high intensity of fractures that strongly affect the production in unconventional shale gas. While most observations of pull-apart basins were from surface mapping or laboratory experiments, we investigated a nascent pull-apart basin in the subsurface. We characterized a nascent pull-apart basin along the strike-slip fault within the Woodford Shale by using seismic attributes analyses, including coherence, dip-azimuth, and curvature. The results indicate a 32 km long, N-S striking strike-slip fault that displays a distinct but young pull-apart basin, which is ~1.6 km by 3.2 km in size and is bounded by two quasi-circular faults. The curvature attribute map reveals two quasi-circular folds, which depart from the main strike-slip fault at ~25°, resulting in an elliptical basin. Inside the basin, a series of echelon quasi-circular normal faults step into the bottom of the basin with ~80 m of total subsidence. We propose that the controls of the shape of pull-apart basin are the brittleness of the shale, and we suggest proper seismic attributes as a useful tool for investigating high fracture intensity in the subsurface for hydrofracturing and horizontal drilling within the shale.

Application of Time-Frequency Decomposition and Seismic Attributes for Stratigraphic Interpretation of Thin Reservoirs in Onshore Fuba Field Niger-Delta, Nigeria

Application of time-frequency decomposition and seismic attributes for stratigraphic interpretation of thin reservoirs in Onshore Fuba Field Niger Delta, Nigeria, are here presented, using Well-log and 3D Seismic data. Well-to-seismic tie, fault mapping, horizon mapping, time surface generation, depth conversion and seismic attributes generation were carried out using Petrel software. 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 and chaos values range from 0.0 to 1.0. The Variance edge and chaos analysis were used to delineate the prominent and subtle faults in the area while the produced spectra were used to delineate the stratigraphy and thickness of the thin-bedded reservoirs. The results of spectral decomposition at frequencies between 12Hz and 35Hz indicate some thin pay sand zones reservoirs which were characterized by of low frequency and high amplitude associated with known hydrocarbon zones, meandering channels, lobes and the presence of small scale faults in the field. Six new probable zones (Prospects A, B, C, D, E and F) of hydrocarbon accumulation were identified. The results of the study will help in the recovery of more hydrocarbon as by-passed zones and subtle structures are revealed in the area of study.

Seismic Coherence Attribute Based on Eigenvectors and Its Application

Seismic Coherence Attribute Based on Eigenvectors and Its Application

Seismic Attribute Gradient Analysis and Reservoir Configuration Study of Shallow Water Delta Reservoir in Huanghekou Sag

Seismic Attribute Gradient Analysis and Reservoir Configuration Study of Shallow Water Delta Reservoir in Huanghekou Sag

Characterization of natural fractures in tight sandstone reservoirs in Yuanba area, northern Sichuan Basin

Natural fractures are quite important for the high and stable production of tight sandstone gas reservoirs. However, it is commonly difficult to accurately characterize their distribution in tight sandstone gas reservoirs due to the strong heterogeneity. A comprehensive characterization of natural fractures in tight sandstone gas reservoirs of the Xujiahe Formation in the Yuanba area, Sichuan Basin, was carried out by multimethod of core description, log interpretation and seismic prediction. The results show that (1) the natural fractures in the Xujiahe Formation are mainly low-angle fractures, with a few medium- and high-angle fractures. The degree of fracture development is mainly controlled by the distance from the fault, the flexural degree of the strata and the thickness of the rock mechanical layer. (2) The fracture-sensitive seismic attributes such as the preferred maximum likelihood, structure entropy, illuminant-based symmetry and high-precision curvature are taken as the input, and the unsupervised cluster analysis algorithm based on Bayesian probability model is applied to predict the distribution of large-scale fractures at different dip angles; on the basis of narrow azimuthal pre-stack CMP gather optimization, the anisotropy detection technique of Hampson-Russell's ProAZ full CMP gather is used to characterize the distribution of small- and medium-scale fractures, and the predicted fracture development intensity matches the fracture interpretation results of the imaging logs by more than 85%. Moreover, the predicted fracture orientation is basically consistent with that of the imaging loggings. (3) The fault-genesis fractures of the Xujiahe Formation are mainly distributed near the fault on the southeast flank of the Jiulongshan anticline, and are distributed in a narrow strip in NE direction. The fold-genesis fractures are mainly developed in the Xu 3rd Member, and are located in the axis of the Jiulongshan anticline and its southeast flank where the strata are strongly flexed. This study provides a solid foundation for the development of tight sandstone gas reservoirs and extends the approaches for the accurate characterization of fractures.

An Intelligent Approach for Gas Reservoir Identification and Structural Evaluation by ANN and Viterbi Algorithm—A Case Study From the Xujiahe Formation, Western Sichuan Depression, China

Gas reservoir identification using seismic data has become a major focus of geophysical exploration. This study presents a gas reservoir identification and structural evaluation method using artificial neural networks (ANNs) and the Viterbi algorithm to improve processing efficiency and evaluate gas reservoir structural control. Initial identification was conducted using deep neural networks (DNNs). Composite seismic attributes sensitive to the multi-component seismic response characteristics of gas reservoirs were obtained. Subsequently, a model expansion dataset and network hyperparameter optimization strategy were employed to assess the optimal DNN model for ReLUactivation with nine hidden layers (3–5–7–7–7–9–9–11–11–11–1). The training model was run with the three composite attributes as input to predict the gas-bearing probability distribution. Considering the importance of evaluating geological structural characteristics, an automatic horizon tracking method using the Viterbi algorithm was proposed to evaluate the structural factors of gas reservoirs. Finally, the ANN-based gas reservoir identification results were comprehensively evaluated based on structural characteristics, thus, reducing the uncertainty, or multiple solutions, predicted by mathematical methods. This scheme was successfully applied to assess synthetic and real data, demonstrating the consistency between the predicted gas reservoir areas and true situation. The effective implementation of this scheme improves processing efficiency and provides a new way to shorten the exploration cycle of a gas reservoir.

Utilizing Seismic Attributes to Enhance Deduction of the subtle stratigraphic trap in Mishrif Formation at Dujaila oil field, Southern East of Iraq

Three seismic instantaneous attributes (phase, frequency, and variance) were utilized on 3D-seismic poststack migrated data, covering 617.31 km2, integrated with data of two wells (Du-1 and Du-2) in Dujaila oil field, southeast of Iraq. They gave good results in detecting reef buildups and confirmed the existence of the stratigraphic hydrocarbon trap that was not obvious in the conventional seismic amplitude sections. They display several seismic criteria in attribute sections for recognizing reef buildups and hydrocarbon accumulation, such as phase reversal, low frequency, and high amplitude variance. The seismic attributes emphasized that the stratigraphic trap of reef rudist buildups with hydrocarbon content is constricted in the upper part of the Mishrif Formation (Cenomanian-Early Turonian) the vicinity around productive well Dujaila-1. It is located at a time from -1730 ms in the Top Mishrif to underneath at a time -1775 ms, which has correspondent the depth 2825 m to 2887.5 m, respectively. This stratigraphic trap covered 70 km2 (length is 10.0 km in direction N-S, and the width is about 7.0 km in the E-W direction). The enclosure of the stratigraphic trap extends about 7 km towards SW from the well Dujaila-1 and did not reach well Dujaila-2 (Du-2). That may explain why it was dry. As a result, the circular region around well Du-1 of a radius of about 4 km, and the area extended about 7 km toward SW from well Du-1 represent a zone of low risk-drill for new exploration wells.

Corrigendum to “Interpretation of Palaeozoic geoforms with the use of seismic attributes in a region of the Eastern Plains, Colombia” [Boletín Geológico, 49(1), 2022

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