Seismic Stack Research Articles

The role of mobile muds in basin form and fill in the tectonic and sedimentologically complex region of the Southeastern Margin of the Caribbean Plate, Trinidad and Tobago, West Indies

The southern portions of the Barbados Accretionary Prism and the eastern deepwater region of Trinidad and Tobago are a seafloor terrain marked by scattered mud volcanoes, mud walls, and mud canopies that rise several hundred meters above the modern seafloor. We use 3D seismic and recently drilled wells to examine this area, dividing it into three tectonomorphic provinces based on the nature of the mud architecture, the structuration, basin types developed, and sedimentary systems occupying the basins. Mud architectures become more massive as one moves northward toward the active tectonic prism. The mud wall in the far northern Mud Wall Province is classified as a mud canopy system, similar to those documented by workers in the North Sabah Pagasa Wedge, Borneo. The Trinidad mud canopy exhibits sutures in the mud mass margins (akin to salt sutures) that separate submasses likely composed of different materials and exhibiting different pressures and properties. These submasses and associated sutures are imageable using the sweetness attributes on the near-stack seismic volume. Anticlines in the isolated mud and distributive provinces, similar to those that are the dominant hydrocarbon-producing structures onshore and offshore Trinidad, show mobile mud cores and are associated with extensional and compressional faulting that provide pathways for hydrocarbon migration into the surrounding lutokinetic stratigraphy. A bottom simulating reflector associated with the base of the gas hydrate zone, extensive across the study area, may be a good indicator of fluid flux activity and subsurface pressure states in mud masses. It is important to use all the seismic data volumes (full, near, mid, and far seismic data stacks) to assess different aspects of these mud diapiric systems.

High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.

Seismic Inversion Techniques and Attribute Analysis for Accurate Well Placement in Offshore Niger Delta Basin, Nigeria

Before this study, simple methods like seismic attribute analysis have often been used for reservoir characterization with successes however, there is still the need to reduce exploration uncertainty to a negligible level and boost investors’ confidence. This study integrated seismic inversion with seismic attribute analysis to better characterize the reservoirs in MTW Field in deep-offshore Niger Delta Basin. Five (5) wells with complete suite of petrophysical logs, three-Dimensional (3-D) seismic data, checkshot and other well information were used. The well data were thoroughly quality checked, reservoirs were litho-stratigraphically delineated and used for petrophysical analysis across the wells. This was followed by seismic-to- well-tie, seismic interpretation, and seismic attribute analysis (Root Mean Square-RMS) generated using depth surface maps. 3-D static reservoir model and volumetric evaluation were carried out. Petrophysical properties were derived and distributed across the 3-D static model using sequential gaussian simulation algorithm to ascertain shale volume spread across the model. To improve the seismic resolution and reduce interpretation uncertainty at greater depth, model- based post-stack seismic inversion was performed to obtain acoustic impedance cube. Litho-stratigraphic and petrophysical analysis result revealed five reservoir sands (A, B, C, D, and E). Reservoir-A was seen to be more viable with a thickness of 13.42 m, high effective porosity of 27%, permeability of 3187.53 mD, low water saturation of 34% and low shale volume of 11% which are indication good reservoir quality and producibility. The seismic interpretation revealed thirty-one (31) growth and antithetic faults oriented in the NE-SW and NW-SE directions, respectively. The RMS result revealed high amplitude reflectivity which is a measure of zone of interest. Based on this, seven (7) prospects and three (3) leads were identified. The seismic inversion result shows a high level of accuracy with a correlation coefficient of 0.997; 0.997; 0.995; and 0.996 in MTW-001, MTW-003ST1, MTW-004ST1 and MTW-005 wells, respectively. The acoustic impedance successfully resolved and improved on the resolution of the seismic stacking velocity especially at reservoir layers and at depth deeper than 3600 ms. Acoustic impedance as a layer property has improved on the lateral and vertical resolutions of the data beyond what the usual seismic interval velocity could image thus, validating some of the prospects and leads identified in this study. This demonstrated that uncertainty can be reduced by a blend of RMS and seismic inversion in identifying reservoir for accurate placement of wells. It is therefore recommended that E and P operators should adopt the technique in their future hydrocarbon exploration endeavor in frontier and matured basins.

Automatic stack velocity picking using a semi‐supervised ensemble learning method

AbstractPicking stack velocity from seismic velocity spectra is a fundamental method in seismic stack velocity analysis. With the increase in the scale of seismic data acquisition, manual picking cannot achieve the required efficiency. Therefore, an automatic picking algorithm is urgently needed now. Despite some supervised deep learning–based picking approaches that have been proposed, they heavily rely on sufficient training samples and lack interpretability. In contrast, utilizing physical knowledge to develop semi‐data‐driven methods has the potential to efficiently solve this problem. Thus, we propose a semi‐supervised ensemble learning method to reduce the reliance on manually labelled data and improve interpretability by incorporating the interval velocity constraint. Semi‐supervised ensemble learning fuses the information of the estimated spectrum, nearby velocity spectra and few‐shot manual picking to recognize the velocity picking. Test results of both the synthetic and field datasets indicate that semi‐supervised ensemble learning achieves more reliable and precise picking than traditional clustering‐based techniques and the currently popular convolutional neural network method.

Quaternary stratigraphy of southern Vietnam continental margin

This study investigates the stratigraphic architectures of the South Vietnam continental margin (SVCM) from the Pliocene to the recent period. We have utilized high-resolution seismic data and sediment core data to achieve this. The study reveals that the Quaternary sequences on the continental margin are bounded by composite erosional surfaces, which were formed due to sub-aerial erosion during periods of sea level fall and subsequent reworking processes during sea level rise. The Quaternary sequence can be divided into two subsequences: the Early Pleistocene and the Middle Pleistocene-Holocene. The Middle Pleistocene-Holocene sequences can be subdivided into six fourth-order sequences, each lasting approximately 100 to 120 thousand years. These sequences are well-preserved on the outer shelf at a modern water depth of around 100 to 120 meters. Seismic stacking patterns indicate that regressive deposits predominantly dominate the Quaternary stratigraphy of the SVCM. This means that sediment deposition during the Quaternary period was more pronounced during periods of sea-level fall. The outer shelf margin of the modern continental margin shows a south-to-southeastward migration of prograding clinoforms, indicating sediment progradation in these directions. The thick Quaternary regressive deposits preserved on the outer shelf are attributed to various factors. These factors include the low shelf gradient, tectonic subsidence, regional hinterland uplift, monsoon strengthening, and high sediment supply from the Palaeo-Mekong Delta during sea-level fall. The interaction of these factors facilitated the accumulation of thick sedimentary deposits during falling sea levels. The mid-late Pleistocene stratigraphy of the SVCM shows similarities to many other continental margins worldwide. These margins are mainly shaped under the influence of asymmetric 4th-order Milankovitch cycles. These cycles are driven by variations in Earth's orbital parameters and are characterized by longer phases of relative sea-level fall followed by shorter periods of stabilization and rise.

Seismic forward modeling for investigating and interpreting thin beds in a carbonate reservoir in SW Iran

High-frequency contents of reflections are essential in the investigation and interpretation of thin-bed reservoirs. These beds can be even more complicated in carbonate rocks, as pore geometries influence final seismic responses. To address these complexities, we propose a seismic forward modeling workflow to investigate several thin-bed reservoirs in a carbonate oilfield with variable pore geometries. The new workflow enhances the existing forward models for the investigation of thin beds by integrating seismic petrophysics, geological model building, and 2D finite-difference elastic modeling. We used seismic petrophysics to ensure the consistency between petrophysical well logs and seismic data using rock physics modeling. Then, we introduced a new high-resolution workflow for velocity modeling to build a reliable geological model. Finally, the 2D finite-difference elastic modeling is employed to generate synthetic traces based on our geological model to obtain seismic responses for the existing thin-bed reservoirs. The forward models used in this study are a powerful tool for investigating thin layers because they enable high-resolution investigation of the given geological model in distinguishing lateral and vertical lithofacies changes. The new velocity modeling workflow, implemented in this research, is more reliable and effective than the conventional velocity property modeling approaches, which resulted in synthetic seismic sections with increased lateral and vertical resolutions and enhanced data from a thin bed. The main features of this workflow are the incorporation of well-log data into geological model building, combining the high-resolution data of horizontal seismic stacking velocity with vertical well logging, and the incorporation of a residual model to improve the seismic stacking velocity. We produced a more coherent section resembling the acquired 3D seismic data by applying the proposed workflow to data from an oil carbonate reservoir in the Fahliyan Formation within the Abadan Plain in SW Iran. It is concluded that the higher frequency synthetic sections from the proposed workflow can assist in resolving the seismic interpretation challenges. By applying the proposed workflow to the current data set, four thin-bed carbonate reservoirs were investigated with corresponding thicknesses of approximately 25, and 17 m at peak frequencies of 60, and 90 Hz, respectively.

Data reconstruction combining MWNI and CRS-based interpolation methods

Many seismic processes, such as migration, inversion, and quantitative interpretation, can be improved via multidimensional regularization and interpolation methods. Different algorithms based on signal processing principles have been proposed for multidimensional seismic data reconstruction. Similarly, the Common Reflection Surface (CRS) stack method, a technique based on ray-theory principles, can also be used for regularization and interpolation. CRS-based reconstruction requires determining the wavefront attributes of the CRS stacking operator directly from the data. These wavefront attributes are commonly searched through global optimization algorithms, where a coherence measure is maximized. The precision and reliability of the wavefront attributes (also called CRS attributes) depend on the optimization method used in their search and the quality of the input data. Onshore field data with missing traces and gaps due to missing shot records can introduce attribute errors and create artifacts that contaminate seismic stacks. To solve this problem, we propose applying the Minimum Weighted Norm Interpolation (MWNI) method for the reconstruction of the prestack data before estimating the CRS attributes. Then we use the wavefront attributes to CRS-based prestack data reconstruction and denoising. We validate the combination of MWNI and CRS-based interpolation methods with synthetic data and show that the CRS attributes extracted by the combined approach are very similar to the reference values. The proposed processing flow shows excellent results on an onshore vintage dataset from the Parnaíba Brazilian basin, where MWNI operates as a fold regularization. The reconstructed data obtained by combining MWNI and CRS show significant improvements compared to the reconstructed data using the two algorithms separately. Prestack time migration images show increased quality when the two methods are adopted in the processing flow.

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.

Prediction of Shale Gas Pressure based on Multi‐channel Seismic Inversion in Fuling

AbstractThe accurate prediction of formation pressure is important in oil/gas exploration and development. However, the achievement of this goal remains challenging, due to insufficient logging data and the low predictive data accuracy from seismic data. In this work, a case study was carried out in the Baima area of Wulong, in order to develop a workflow for accurately predicting shale gas formation pressure. The multi‐channel stack method was first used, as well as the inversion of single‐channel seismic data, to construct velocity and density models of the formation. Combined with the existing well‐logging data, the velocity and density models of the whole well section were established. The shale gas formation pressure was then estimated using the Eaton method. The results show that the multi‐channel seismic stacking method has a higher accuracy than the inversion of the formation velocity obtained by the single‐channel seismic method. The discrepancies between our predicted formation pressure and the actual formation pressure measurement are within an acceptable range, indicating that our workflow is effective.

Convolution neural network application for first‐break picking for land seismic data

ABSTRACTAn automatic and robust algorithm for the first‐break picking is necessary to build the near‐surface velocity model. We propose the algorithm based on a convolution neural network. The introduced first‐break picking strategy and neural network architecture are suited for processing large volumes of seismic exploration data with reasonable computational resources. To develop an optimal neural network topology and architecture, extensive testing was performed. We compared several architectures of neural networks, including one‐ and two‐dimensional approaches. Our tests justify that the one‐dimensional approach (trace‐by‐trace processing) provides the most reliable results in the case of first‐break travel‐time variations typical of complicated near‐surface structures. This study demonstrates that the four‐layered neural network trained on 5,000 traces is enough for robust first‐break picking. The algorithm is evaluated on two land‐acquisition field datasets from West Siberia with a total used size of about 7 million traces. The first dataset is used for training, and the second one is used only for testing. For both datasets, the error between the original and the predicted first breaks is not more than three samples for 95% of traces. The final evaluation is done by a comparison of seismic stacks to prove the benefits of the approach and its robustness for offsets over 600 m. Finally, the influence of choosing the locations for the training dataset is discussed, and a strategy for using the proposed approach in production work is introduced.

Enhancing body waves in passive seismic reflection exploration: A case study in Inner Mongolia, China

Among all geophysical exploration methods, seismic exploration is undoubtedly the most important due to its ability to allow depth exploration at high resolutions. Traditionally speaking, the method needs an active seismic source, such as dynamite, to generate energy and perform reflection and refractions. An active source usually means high cost, and it also can be quite difficult to implement when surface conditions are particularly complex. The use of passive seismic for reflection exploration does not require an active seismic source. It has the potential of providing a low-cost alternative technique in some exploration areas. The main issues related to passive-source body-wave exploration include suppressing the surface waves retrieved from sources located at or near the surface, as well as enhancing the body waves from random sources at depth. We address these problems by developing a preprocessing workflow to suppress the surface waves and the other unwanted coherent noise events in the original data without seriously affecting the remaining body waves. We also propose a method for separating surface and body waves based on the signal-to-noise ratio of the frequency-domain signals. Next, we use crosscorrelation to generate virtual shot gathers, just like the active ones, and use conventional seismic data processing steps to generate the final seismic imaging. By analyzing the passive data set collected from Inner Mongolia, we have verified the applicability of the proposed method, and the retrieved final stack section indicates good consistency with an active seismic stack section along the same line. Accordingly, we assert that the application of this data processing method will contribute to the body-wave imaging and the inversion analysis of passive seismic records.

A continuous towed seismic data acquisition and correlation stack method for rapid detection of urban road

The rapid detection of urban road disasters has become an urgent problem. The continuous towed geophysical method is an important method for rapid general detection. Ground-penetrating radar and transient electromagnetic have realized continuous towed detection, but the seismic method cannot do it. The reflection seismic method has the advantages of deep detection depth and high resolution. To adapt to the rapid deployment of geophones on urban hardened pavement, a gravity geophone was designed to realize towed acquisition. However, the data acquisition is far from meeting the needs of large area rapid detection on urban roads. In this paper, a continuous towed seismic data collection method is proposed for the first time, that is, the vibration generated by the friction between the geophone base and the ground is used as the seismic source to realize the rapid data acquisition on the urban roads. When the vehicle-mounted seismic equipment is used and the required lateral resolution is 0.5 m, the continuous towed seismic data acquisition efficiency is about 5 km/h. Continuous towed seismic data has a low-resolution and low signal-to-noise ratio, and the real weak reflected signal is drowned by strong energy noise. To solve these problems, this paper proposes a correlation stack method to extract the stack section. When seismic data is collected with the continuous towed seismic equipment, the seismic source signal is not the approximate sharp plus signal, so the resolution of the extracted stack section is still low. Single detection results are difficult to distinguish near-surface structures. The continuous towed seismic acquisition method has a low cost and can realize the rapid repeated detection of the same survey line. The model data and measured data prove that the proposed data acquisition scheme and data processing method have the following advantages: 1) The continuous towed seismic acquisition method can improve the data acquisition efficiency, reduce the cost of data acquisition, and is suitable for large area rapid detection. 2) The correlation stack method can extract reflected signals with real weak energy. 3) Stack processing for multiple detection data can improve the resolution and enhance the energy of real weak signals. The continuous towed seismic data acquisition and the data processing method can realize the rapid inspection and detection of urban roads and has practical engineering application value.

Seismic imaging and recognition technology of medium and shallow channel sand body

The fine characterization of channel sand body is a complex system engineering. The longitudinal superposition relationship of channel is complex, the sandstone thickness is thin, and the lateral changes of lithology and physical properties are obvious. It is necessary to carry out the research on the integration of processing and interpretation and seismic geology. The key technologies of seismic high-resolution pre stack and post stack inversion of medium and shallow channel sand bodies with “improved HHT point spectrum whitening” and “weak sidelobe wavelet structure” as the core and “azimuth well control inversion modeling” and “Cauchy regularization high-resolution inversion” as the core have been formed through experimental research.

Prediction of Gamma Ray data from pre-stack seismic reflection partial angle stacks using Continuous Wavelet Transform and convolutional neural network approach

Seismic inversion is one of the critical issues for geophysicists in the oil and gas industry. It is commonly used to estimate the distribution of facies-types and fluids in reservoirs across the value chain from exploration to development. However, the low vertical and spatial resolution of seismic data leads to large uncertainties in these estimations, which makes direct use of inversion results challenging. The objective of this study was the estimation of a 3D volume of Gamma Ray data from pre-stack seismic reflectivity partial angle stacks based on the Convolutional Neural Network architecture by representing data through the Continuous Wavelet Transform. The proposed methodology used 132 deviated wells from the Azeri and Chirag parts of the Azeri – Chirag – Gunashli field in the South Caspian Basin as training data. Blind tests show promising Gamma Ray predictions. To optimize the predictions, sensitivity analysis was performed on the Continuous Wavelet Transform parameters (wavelet types, range of pseudo-frequencies, etc.), the Convolutional Neural Network architecture (number of convolutional and pooling layers, dropout, activation functions) and the network parameters (batch size, number of epochs, optimizers, etc.). Due to the vast amount of data required to speed up the learning process, simulations were performed on a Graphics Processing Unit based on the Compute Unified Device Architecture platform.

Geometry of the Décollement Below Eastern Bangladesh and Implications for Seismic Hazard

AbstractEastern Bangladesh sits on the seismically active Chittagong‐Myanmar fold and thrust belt (CMFB), a north‐trending accretionary wedge on the eastern side of the India‐Eurasia collision. Earthquakes on the basal décollement and associated thrusts within the CMFB present a hazard to this densely populated region. In this study, we interpret 28 seismic reflection profiles from both published and unpublished sources to constrain the depth of the basal décollement. To convert profiles from the time domain to the depth domain, we integrate sonic log and seismic stacking velocity data to generate time‐velocity relationships for different parts of the CMFB. Our analysis reveals that the décollement is ∼9 km deep in northeast and southeast Bangladesh, but shallows to ∼5 km in east‐central Bangladesh. The décollement has an area of 7.25 × 104 km2 (∼150 × 450 km), making it capable of an Mw 8.5 earthquake. However, the warped geometry of this fault might act as a rupture barrier were a large earthquake to occur on the décollement. Our combined velocity and fault model lay the groundwork for future studies to address seismic segmentation, ground shaking, and rupture modeling in the CMFB. Finally, we use our compiled data set to analyze the evolution of fold kinematics in the CMFB. We observe that folding style and failure mode varies, from mainly ductile deformation in the foreland to mainly brittle in the hinterland. The dual‐failure modes within the CMFB support the hypothesis that a region with ductile deformation may still be capable of seismic behavior.

Bayesian facies inversion on a partially dolomitized isolated carbonate platform: A case study from Central Luconia Province, Malaysia

We have developed a case study of geophysical reservoir characterization in which we use elastic inversion and probabilistic prediction to estimate nine carbonate lithofacies and the associated porosity distribution. The study focuses on an isolated carbonate platform of middle Miocene age, offshore Sarawak in Malaysia that has been partly dolomitized — a process that increased the porosity and permeability of the prolific gas reservoir. The nine lithofacies are defined from one reference core and include a range of lithologies and pore types, covering limestone and dolomitized limestone, each with vuggy varieties, as well as sucrosic and crystalline dolomites with intercrystalline porosity, and argillaceous limestones and shales. To predict the lithofacies and porosity from geophysical data, we adopt a probabilistic algorithm that uses Bayesian theory with an analytical solution for conditional means and covariances of posterior probabilities, assuming a Gaussian mixture model. The inversion is a two-step process, first solving for P- and S-wave velocities and density from two partial seismic stacks. Subsequently, the lithofacies and porosity are predicted from the elastic parameters in the borehole and across a 2D inline. The final result is a model that consists of the pointwise posterior distributions of the facies and porosity at each location where seismic data are available. The facies posterior distribution represents the facies proportions estimated from seismic data, whereas the porosity distribution represents the probability density function at each location. These distributions provide the most likely model and its associated uncertainty for geologic interpretations of lithofacies associated with distinct stages of carbonate platform growth.

Machine-Learning Method Determines Salt Structures From Gravity Data

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201424, “Machine-Learning Method To Determine Salt Structures From Gravity Data,” by Jie Chen, Cara Schiek-Stewart, and Ligang Lu, Shell, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, 5-7 October. The paper has not been peer reviewed. In the complete paper, the authors develop a machine-learning (ML) method to determine salt structures directly from gravity data. Based on a U-net deep neural network, the method maps the gravity downward continuation volume directly to a salt body mask volume, which is easily interpretable for an exploration geophysicist. The authors conclude that the ML-based method from gravity data complements seismic data processing and interpretation for subsurface exploration. Introduction In subsurface exploration, seismic is the dominant method used to reconstruct the underground image for geophysicists and geologists to locate possible hydrocarbon reservoirs. Seismic acquisition is carried out by human-induced sound waves (by airgun or vibrators) that are recorded, once reflected, on the surface. Through the iterative waveform inversion process, a subsurface image can be reconstructed for reservoir location and property determination. Nonseismic (gravity and magnetic-measurement) methods, on the other hand, are passive measurements and not intrusive to the environment. In gravity data acquisition, gravimeters measure the change in the gravitation-al field, which can be used to determine the density variation on the subsurface. Compared with seismic acquisition, gravity acquisition is cheaper and introduces a much smaller carbon footprint. Gravity data resolution is, in principle, worse than that of seismic. However, especially in areas of salt structures, gravity data provide a unique addition because the density contrast between salt and the surrounding sediments in-creases with depth, while the velocity contrast decreases with depth. Therefore, gravity data provide valuable additional constraints in salt delineation for interpretation and seismic processing. Recently, ML and deep-learning (DL) applications in hydrocarbon exploration have been studied extensively. The authors note developments such as use of ML/DL on seismic data noise attenuation, salt interpretation from seismic stack, least-square inversion, rock-facies classification, and 4D seismic in reservoir management. To the authors’ knowledge, no literature exists that explores use of ML on nonseismic data. The authors’ method can map the gravity downward continuation volume directly to a salt body mask (0/1 for nonsalt/salt) volume, which saves iterative effort of the conventional gravity inversion process and is easily interpretable for explorational geophysicists and geologists. Gravity Data Processing Raw gravity data are measured as a 2D Bouguer anomaly (the difference between measured gravity and theoretical gravity value) grid. The first step of gravity inversion is to perform a downward continuation calculation to generate a 3D volume so that the depth of the density anomaly can be estimated. The equivalent source technique is one of the more-stable downward continuation calculations and is a preferred method for making downward continued volumes used in in-field reference drilling.

Seismic-while-drilling applications from the first DrillCAM trial with wireless geophones and instrumented top drive

Advanced geophysical sensing while drilling is being driven by trends to automate and optimize drilling and the desire to better characterize complex near surface and overburden in desert environments. We introduce the DrillCAM system, which combines a set of geophysical techniques from seismic while drilling (SWD), drill-string vibration health, estimation of formation properties at the bit, and imaging ahead of and around the bit. We present data acquisition, processing, and initial application results from the first field trial on an onshore well in a desert environment. In this study, we focus on SWD applications. For the first time, wireless geophones installed around a rig were used to acquire continuous data while drilling. We demonstrate the feasibility of such a system to provide flexible acquisition geometries that are easily expandable with increasing bit depth without interference from drilling operations. Using a top-drive sensor as a pilot, we transform the drill-bit noise into meaningful and reliable seismic signals. The data were used to retrieve a check shot while drilling, make kinematic look-ahead predictions, and obtain a vertical seismic profiling corridor stack matching surface seismic. Robust near-offset check-shot signals were received from roller-cone and polycrystalline diamond compact (PDC) bits above 7200 ft after limited preprocessing of challenging single-sensor data with supergrouping. Detecting signals from deeper sections drilled with PDC bits may require more advanced processing by using an entire 2D spread of wireless geophones and downhole pilots. The real-time capabilities of the system make the data available for continuous data processing and interpretation that will facilitate drilling automation and improve real-time decision making.

An integrated petrophysical and geomechanical characterization of Sembar Shale in the Lower Indus Basin, Pakistan, using well logs and seismic data

Shale gas reservoirs are generally exploited through horizontal drilling and hydraulic fracturing. Petrophysical and geomechanical parameters help to determine horizontal well orientation, to select drilling mud densities for stable drilling operations, to assess the suitability of specific zones in a formation for hydraulic fracturing, and to assess required hydraulic fracturing pressures. The primary objective of this work was to characterize a shale interval in the Early Cretaceous-age Sembar Formation in the Lower Indus Basin of Pakistan, using only readily available data. A workflow was developed for the estimation and mapping of geomechanical properties using logs from multiple wells and relevant post-stack seismic reflection data. Mineralogy data from well cuttings, core testing results for elastic properties and hydraulic fracturing test data (mostly obtained from one key well) were utilized to constrain the values of the properties estimated from geophysical data. The following results obtained at the well-scale suggest that the Sembar Shale is favorable for development: high gas saturation, good porosity (up to 10%), moderate quantity of thermally mature organic matter (2%–4% TOC), a number of brittle intervals separated by thicker intervals that fall slightly below the brittle-ductile threshold, and a strike-slip stress regime. At the scale of the study area, robust statistical techniques were used to invert seismic stacks and develop a 3D mechanical earth model. This model shows a trend of increasing shale brittleness towards the northeastern portion of the study area, hence suggesting that this area might be most prospective for initial shale gas development. The results of sensitivity analyses are presented, which illustrate the potential errors in the estimated geomechanical properties. Future work to improve confidence in the shale gas potential of the Sembar Formation is given, including extensive coring and laboratory testing, in-situ stress and natural fracture characterization, and better delineation of shale thickness.

Seismic Behavior of Steel-Concrete Composite Structures

Steel-Concrete composite individuals are an intriguing alternative for auxiliary originators, yet the dependability of plan strategies both on account of gravity and seismic burdens is in persistent advancement. Composite steel-concrete design gives a noteworthy economy through decreased materials and quicker development, such framework utilizes each kind of part in the most proficient way to expand the basic and financial advantages. In this undertaking, hypothetical, numerical perspectives and applications concerning the seismic conduct of steel-concrete composite structures are to be dissected. The intrigue has been concentrating around there on the capacity of composite encircled structures to disseminate seismic vitality by methods for inelastic disfigurements with the goals to: (1) Apply non-direct investigation strategies to assess building execution. (2) The chief highlights influencing the seismic reaction of composite edges. (3) A numerical examination has been direct to research the impact of composite conduct of the structures. (4) A limited component modular has been create to represent the dynamic conduct of composite structure and (5) The impact of shear connector on the conduct of composite encircled structure in seismic stacking.