Seismic Multi-attribute Analysis Research Articles

Automatic Evaluation of an Interwell-Connected Pattern for Fractured-Vuggy Reservoirs Based on Static and Dynamic Analysis

The types of fractured-vuggy reservoirs are diverse, with dissolution holes and fractures of different scales as the main reservoir spaces. Clarifying the connectivity between wells is crucial for improving the recovery rate of fractured-vuggy reservoirs and avoiding problems of poor water- flooding balance and serious water channeling. A traditional dynamic connected model hardly describes the geological characteristics of multiple media, such as karst caves and fractures, which cause multiple solutions from the calculation. The static analysis is the basis for connectivity evaluation. In this study, we designed an intelligent search strategy based on an improved A* algorithm to automatically find a large-scale fractured-vuggy connected path by seismic multi-attribute analysis. The algorithm automatically evaluates the interwell-connected mode and clarifies the relationship between the static connected channel and the fractured-vuggy space configuration. Restricted by various factors, such as seismic identification accuracy, a static connectivity study can hardly determine the filling and half-filling inside the channel effectively, even if it can identify the main connectivity channels. An injection-production response analysis based on dynamic production data can more accurately reflect the reservoir’s actual connectivity and degree of filling. This paper further studies dynamic response characteristics based on multifractals combined with production data. To reduce the evaluation uncertainty, we combined the static and dynamic connected analysis results to comprehensively evaluate the main connected modes, such as large fracture connectivity, cavern connectivity, and fractured-vuggy compound connectivity. We use the Tahe oilfield as an example to carry out an automatic evaluation of the connected pattern. The comprehensive evaluation results of the new algorithm were basically consistent with the tracer test results and can better reflect the interwell space-configuration relationship. Our model has certain guiding significance for the adjustment of working measures during waterflooding in fractured-vuggy reservoirs.

The Control of Sea Level Change over the Development of Favorable Sand Bodies in the Pinghu Formation, Xihu Sag, East China Sea Shelf Basin

The Pinghu Formation consists primarily of marine-continental transitional deposits. The widely distributed fluvial and tidal transgressive sand bodies comprise the main reservoirs of the Baochu slope zone in the Xihu Sag in the East China Sea Shelf Basin. These sand bodies are deeply buried, laterally discontinuous, and are frequently interrupted by coal-bearing intervals, thereby making it extremely difficult for us to characterize their hydrocarbon potential quantitatively via seismic inversion techniques, such as multi-attribute seismic analysis and post-stack seismic inversion, hindering further hydrocarbon exploration in the Xihu Sag. Here, a prestack seismic inversion approach is applied to the regional seismic data to decipher the spatiotemporal distribution pattern of the sand bodies across the four sequences, i.e., SQ1, SQ2, SQ3, and SQ4, from bottom up, within the Pinghu Formation. In combination with detailed petrology, well log, and seismic facies analysis, the secular evolution of the sedimentary facies distribution pattern during the accumulation of the Pinghu Formation is derived from the sand body prediction results. It is concluded that the sedimentary facies and sand body distribution pattern rely on the interplay between the hydrodynamics of fluvial and tidal driving forces from the continent and open ocean, respectively. Drops in the sea level led to the gradual weakening of tidal driving forces and relative increases in riverine driving forces. The direction of the sand body distribution pattern evolves from NE–SW oriented to NW–SE oriented, and the dominant sand body changes from tidal facies to fluvial facies. In addition, the sea level drop led to the decrease in the water column salinity, redox condition, organic matter composition, and the development of coal seams, all of which directly influenced the quality of reservoir and source rocks. The sand bodies in SQ2 and SQ3 are favorable reservoirs in the Pinghu Formation due to their good reservoir properties and great thickness. The high-quality source rock in SQ1 could provide significant hydrocarbons and get preserved in the sand body within SQ2 and SQ3. This contribution provides an insight into the control of the sea level change over the development of hydrocarbon reservoirs in the petroleum system from marginal-marine environments such as the Xihu Sag.

Technology Focus: Seismic (February 2022)

The past couple of years has been a rollercoaster for energy professionals, but it did not stop the incredible achievements in machine-learning techniques, particularly neural networks to improve seismic imaging and multiple attribute extraction to decipher reservoir rock properties and fluid contact movements in areas with data paucity and increasing difficulty in finding new or remaining hydrocarbon accumulations. As I reviewed the seismic-related papers, I was impressed at the innovative ML methods that were developed and successfully deployed to improve already existing data and augment sometimes scarce data to aid understanding of risks and uncertainties, enhance hydrocarbon recovery, reduce cost, increase production, and, in some cases, redefine the geological understanding of the play. Apart from the fields of seismic imaging, reservoir characterization, and hydrocarbon recovery, there were many papers devoted to new tectonic models, improved geohazards assessment, microporosity estimates, and data-acquisition methods—vertical seismic profiles, shear wave, 3D/4D ocean bottom cable, electromagnetic survey, and high-resolution broadband seismic. It was difficult to select the top three papers for synopsis because there were so many interesting topics to choose from, but I ensured that readers receive a taste of the major topics covered, selecting some that are must-reads and recommending a few others for your reading pleasure. I hope you find them as enjoyable as I did. Recommended additional reading at OnePetro: www.onepetro.org. IPTC 19810 - Hydrocarbon Exploration and Production Potential of the Gulf of Suez Basin in the Framework of the New Tectonostratigraphic Model by Janpieter Van Dijk, Dragon Oil, et al. OTC 30620 - Improving Drilling Geohazards Assessments of the Overburden Section by Performing Seismic Multiattribute Analysis by Yosmel Sanchez, Fugro, et al. SPE 200761 - Accounting for Model Errors of Rock Physics Models in 4D Seismic History-Matching Problems: A Perspective of Machine Learning by Xiaodong Luo, NORCE, et al. SPE 202651 - The Houta Prospect Hammamet Basin, Offshore Tunisia, as an Example of a New Play in an Old Basin by Janpieter van Dijk, GGS, et al. SPE 202753 - A Machine-Learning Approach to Removing Highly Variable Surface Waves for a Large Shallow-Water Survey Offshore Abu Dhabi by Al Hajeri Badreia, ADNOC, et al.

Use of Seismic Spectral Decomposition, Phase, and Relative Geologic Age as Attributes to Improve Quantitative Porosity Prediction in the Daqing Field, China

The high production potential of the Daqing oilfield in China is recognized for seismically thin sand bodies that usually are not resolved with conventional seismic data. The present study assesses the usefulness of applying seismic multi-attribute analysis to bandwidth extended data in resolving and making inferences about these thin layers. In thin layers, tuning can obscure relationships between seismic amplitude and rock properties. In such cases, the seismic phase varies with the layer impedance and may hence aid in reservoir characterization. A seismically derived relative geologic age may also be a useful attribute in predicting rock properties because it helps define the stratigraphic position of a layer. When utilized in multi-attribute analysis in the Daqing field, spectral decomposition amplitude, phase, and a relative geological age attribute to improved prediction of well log effective porosity from seismic data and are preferentially selected by stepwise regression. The study follows standard methodology by implementing seismic multi-attribute analysis and discusses the improvement of applying it to bandwidth extended data. This will include a combination of attributes such as relative geologic age, phase, amplitude, and the magnitude components of spectrally decomposed data.

Enhancing Paleoreef Reservoir Characterization through Machine Learning and Multi-Attribute Seismic Analysis: Silurian Reef Examples from the Michigan Basin

Historically, Silurian pinnacle reef complexes in the Michigan Basin have been largely identified using 2D seismic with very little research on the reservoir characterization of these reefs using 3D seismic data. By incorporating a high-resolution 3D dataset constrained by a well-studied and data-rich paleoreef reservoir, the Puttygut reef, seismic attributes were correlated to petrophysical properties through machine learning and self-organizing maps (SOMs). A suite of structural and frequency-based attributes was calculated from pre-stack time migrated (PSTM) seismic data, with only a subset of them selected as SOM inputs. Structural attributes enhanced details in the reef but frequency attributes were overall more useful for correlating with reservoir quality. A strong relationship between certain combination percentages of attributes and certain sections of the reef with porosity and permeability was found after the SOM results were compared to wireline log and core analysis data. Areas with high permeability and porosity correlated with the average frequency and spectral decomposition at 29 and 81 Hz. Areas with high porosity and varying permeability correlated with the average frequency and spectral decomposition at 29, 57, and 81 Hz. Areas with intermediate porosity correlated with the average frequency and spectral decomposition at 29 and 57 Hz. The efficacy of the procedure was then demonstrated on two nearby reefs with very similar results.

The application of volume texture extraction to three-dimensional seismic data – lithofacies structures exploration within the Miocene deposits of the Carpathian Foredeep

There are numerous conventional fields of natural gas in the Carpathian Foredeep, and there is also evidence to suggest that unconventional gas accumulations may occur in this region. The different seismic sig-natures of these geological forms, the small scale of amplitude variation, and the large amount of data make the process of geological interpretation extremely time-consuming. Moreover, the dispersed nature of information in a large block of seismic data increasingly requires automatic, self-learning cognitive processes. Recent developments with Machine Learning have added new capabilities to seismic interpretation, especially to multi-attribute seismic analysis. Each case requires a proper selection of attributes. In this paper, the Grey Level Co-occurrence Matrix method is presented and its two texture attributes Energy and Entropy. Haralick’s two texture parameters were applied to an advanced interpretation of the interval of Miocene deposits in order to discover the subtle geological features hidden between the seismic traces. As a result, a submarine-slope channel system was delineated leading to the discovery of unknown earlier relationships between gas boreholes and the geological environment. The Miocene deposits filling the Carpathian Foredeep, due to their lithological and facies diversity, provide excellent conditions for testing and implementing Machine Learning techniques. The presented texture attributes are the desired input components for self-learning systems for seismic facies classification.

Seismic prediction of soil distribution for the Chang-Bin offshore wind farm in the Taiwan Strait

Direct soil measurements are limited to borehole locations and are therefore sparse in the oceans. To effectively characterize the soil distributions for the Chang-Bin offshore wind farm, which is an area with the greatest wind energy potential in the Taiwan Strait, we have developed a workflow to predict the soil distribution in the subsurface based on integrated analysis of seismic data and borehole data. First, we characterize the key seismic units and their seismic response in order to understand the regional stratigraphy. Then, we correlate the soil types to each stratigraphic unit as the constraint for the input and quality control to train a neural network based on seismic multiattribute analysis. Finally, we develop a neural network that is suitable for soil prediction in the Chang-Bin offshore wind farm. Five seismic units identified from the seismic profiles reveal that the regional stratigraphy has been greatly affected by sea-level change and the sediment transportation process. Confirmed by independent in situ borehole data, the neural network is considered reliable up to 60 m below the seafloor, whereas decreased signal-to-noise ratios at greater depths lead to poorer prediction accuracy. Compared to previous studies that mainly are based on high-quality 3D seismic and well logging data, our method can predict the soil distribution by analyzing 2D seismic profiles and simplified soil layers alone. The prediction results reveal detailed lithologic variations that are tested by in situ borehole measurements. Therefore, we are confident that this approach could effectively obtain the soil distribution prediction and thus reduce the costs in offshore engineering applications.

The early Paleozoic structures and its influence on the Permian strata, Midland Basin: Insights from multi-attribute seismic analysis

The Permian Basin is one of the most important petroleum-producing regions in the United States. The structural history of the Permian Basin is extremely complex. Most of the structural traps in the Permian Basin are associated with the strike-slip fault system, which presented challenges for past structural interpretation completed primarily using well log and 2D seismic data. We utilized a 3D seismic survey, a part of the Midland Basin and eastern margin of the Central Basin Platform (CBP), to identify the Early Paleozoic structural elements to study its influence on the HC production from the Permian strata. We computed volumetric seismic attributes including, coherence which can identify faults with a significant amount of throw, along with curvature which can detect flexure and folds. We also used, recently developed aberrancy attribute, which illuminates subtle faults and flexures. We observed several strong lineations on seismic attributes extracted along the Woodford surface. Lineations have three different trends. Trend 1 lineations are drag folds associated with the Andrews shear zone (ASZ). Trend 2 lineations, which show a left-lateral strike-slip motion, are R’ Riedel Shear, are oriented N 65° W truncating against the CBP. The trend 2 lineations continue upward to the shallower surfaces, as well as they are parallel to the surface lineations identified on the satellite images, which indicates the continued movement of the pre-existing faults. The trend 3 lineations, which show a right-lateral strike-slip motion, are R Riedel Shear and align with the ASZ. The formation micro image (FMI) shows the fracture orientations in trend 2 and trend 3 directions. The aberrancy lineation anomalies are indicative of highly fractured areas; wells drilled in these areas display lower oil-water ratio, as well as a higher amount of production, which may be due to the increase in permeability due to the fractures.

Case study: Seismic resolution and reservoir characterization of thin sands using multiattribute analysis and bandwidth extension in the Daqing field, China

The Daqing field, located in the Songliao Basin in northeastern China, is the largest oil field in China. Most production in the Daqing field comes from seismically thin sand bodies with thicknesses between 1 and 15 m. Thus, it is not usually possible to resolve Daqing reservoirs using only conventional seismic data. We have evaluated the effectiveness of seismic multiattribute analysis of bandwidth extended data in resolving and making inferences about these thin layers. Multiattribute analysis uses statistical methods or neural networks to find relationships between well data and seismic attributes to predict some physical property of the earth. This multiattribute analysis was applied separately to conventional seismic data and seismic data that were spectrally broadened using sparse-layer inversion because this inversion method usually increases the vertical resolution of the seismic. Porosity volumes were generated using target porosity logs and conventional seismic attributes, and isofrequency volumes were obtained by spectral decomposition. The resulting resolution, statistical significance, and accuracy in the determination of layer properties were higher for the predictions made using the spectrally broadened volume.

Seismic multi-attribute analysis for petrophysics reservoir prediction with probabilistic neural network in “FA” field

Oil and gas reserves are increasingly difficult to find due to more complex geological conditions. This complex condition causes difficulties in determining reservoir distribution. Therefore, a better method is needed to overcome these complex geological conditions. In this study, the petrophysics analysis by using the multi-attribute and the Probabilistic Neural Network (PNN) used to make reservoir distribution model on seismic horizontal slice. This multi-attribute method and Probabilistic Neural Network (PNN) that can search for correlation between seismic attributes and the data sought, for the prediction of property values from surrounding rocks. From this method, the distribution of porosity data with a correlation value of 0.52 was generated, water saturation with a correlation value of 0.73, and shale content with a correlation value of 0.58. Where the combination of porosity data, water saturation, shale content, and acoustic impedance (AI) data of inversion results can be a clue to identify reservoir distribution. From the porosity and saturation values, hydrocarbon dispersion can be made, wherein this study values were obtained between 0.01 0.03. This “FA” field has a reservoir between wells F-06, FA-05, FA-15, and FA-18 and spreads westward from wells FA-05, FA-15 & FA-18. The distribution of petrophysical parameters generated from the validation of well data using the multi-attribute method. This thing prove that Multi-attribute and neural network analysis can be used to determine predictions of porosity, water saturation, and shale content well and can be used for reservoir characterization.

Net reservoir discrimination through multi-attribute analysis at single sample scale

Abstract Self-Organizing Map (SOM) is an unsupervised neural network — a form of machine learning — that has been used in multi-attribute seismic analysis to extract more information from the seismic response than would be practical using only single attributes. The most common use is in automated facies mapping. It is expected that every neuron or group of neurons can be associated with a single depositional environment, the reservoir’s lateral and vertical extension, porosity changes or fluid content (Marroquin et al., 2009). Of course, the SOM results must be calibrated with available well logs. In this paper, the authors generated petrophysical labels to apply statistical validation techniques between well logs and SOM results. Based on the application of PCA to a larger set of attributes, a smaller, distilled set of attributes were classified using the SOM process to identify lithological changes in the reservoir (Roden et al., 2015).

Multiattribute Seismic Analysis With Fractal Dimension and 2D and 3D Continuous Wavelet Transform

Summary The 3D post-stack seismic attributes provide an intuitive and effective way of using seismic volumes for reservoir characterization and development, and further identification of exploration targets. Some of the seismic attributes can aid in the precise prediction of the geometry and heterogeneity of subsurface geological settings. These also can provide useful information on petrophysical and lithological properties when combined with well-log information. There exist numerous seismic attributes that provide a unique interpretation on some aspects of subsurface geology. Of these, the proper demarcation of structural features— such as location and edges of faults and salt domes, and their throw and extent—always has been of primary concern. In this paper, we propose new multiattribute seismic algorithms by using fractal dimension and 2D/3D continuous wavelet transform (CWT). The use of higher-dimensional wavelets incorporates information from the ensemble of traces and can correlate information between neighboring traces in seismic data. The spectral decomposition that is based on the CWT aids in resolving various features of geological interest at a particular scale or frequency, which, when rendered with fractal attribute, demarcates the boundaries between those. We apply these two algorithms separately to a seismic amplitude volume and co-render output volumes together with some weights to yield a final attribute volume incorporating information from the aforementioned algorithms. We demonstrate the efficacy of these two algorithms in terms of the resolution and proper demarcation of various geological structures on real seismic data. The application of these algorithms results in better illumination and proper demarcation of various geological features such as salt domes, channels, and faults, and it illustrates how these simple tools can help to extract detailed information from seismic data.

Seismic multiattribute analysis for shale gas/oil within the Austin Chalk and Eagle Ford Shale in a submarine volcanic terrain, Maverick Basin, South Texas

We conducted seismic multiattribute analysis by combining seismic data with wireline logs to determine hydrocarbon sweet spots and predict resistivity distribution (using the deep induction log) within the Austin Chalk and Eagle Ford Shale in South Texas. Our investigations found that hydrocarbon sweet spots are characterized by high resistivity, high total organic carbon (TOC), high acoustic impedance (i.e., high brittleness), and low bulk volume water (BVW), suggesting that a combination of these log properties is required to identify sweet spots. Although the lower Austin Chalk and upper and lower Eagle Ford Shale intervals constitute hydrocarbon-sweet-spot zones, resistivity values and TOC concentrations are not evenly distributed; thus, the rock intervals are not productive everywhere. Most productive zones within the lower Austin Chalk are associated with Eagle Ford Shale vertical-subvertical en echelon faults, suggesting hydrocarbon migration from the Eagle Ford Shale. Although the quality factor (Q) was not one of the primary attributes for predicting resistivity, it nevertheless can serve as a good reconnaissance tool for predicting resistivity, brittleness, and BVW-saturated zones. In addition, local hydrocarbon accumulations within the Austin Chalk may be related to Austin TOC-rich zones or to migration from the Eagle Ford Shale through fractures. Some wells have high water production because the water-bearing middle Austin Chalk on the downthrown side of Eagle Ford Shale regional faults constitutes a large section of the horizontal well, as evidenced by the Q attribute. Furthermore, the lower Austin Chalk and upper Eagle Ford Shale together appear to constitute a continuous (unconventional) hydrocarbon play.

Seismic interpretation of tectonic and paleogeomorphologic controls on sediment dispersal patterns in a continental rift basin: A case study from the Bohai Bay Basin, China

Tectono-paleogeomorphologic frameworks exert a major control on sediment dispersal patterns and types of depositional systems, both of which influence reservoir geometry and quality. In this study, we analyze the framework of the [Formula: see text] Member of the Dongying Formation in QHD29-2 Block, Bohai Bay Basin, China. The framework is constructed through (1) conventional sedimentologic study of single-well facies, cumulative frequency curves, and thin section analysis, (2) seismic stratigraphic interpretation of sectional seismic facies, and (3) seismic sedimentologic mapping through stratal slicing and seismic multiattribute analysis. The structural system is marked by a master boundary fault in the Shijiutuo Uplift that has greater displacement at its center than at the propagating fault tips. The resulting paleogeomorphologic architecture is characterized by a steep slope in the central part of the boundary fault and two gentle slopes at fault tips. Sediment dispersal patterns indicate that fan-shaped and lobate proximal fan deltas were rapidly deposited in the central steep slope and belt-shaped distal river deltas were deposited in the gentle slopes after long-distance transport. Proposed sediment dispersal patterns for the central fault segment and the propagating fault tips should serve as a useful reference for the stratal geometry, depositional patterns, distribution of depositional facies and of potential reservoir and source rocks, and evaluation of hydrocarbon reservoirs in extensional rift basins in eastern China and similar basins around the world.

Multi-attribute seismic analysis - tackling non-linearity

Later, instead of neural networks, a different mathematical approach using cubic b-splines was utilized for the same purpose. The results were found to be similar, suggesting that apart from neural networks, the cubic b-splines could be used as a tool for tackling non-linearity in multi-attribute seismic analysis. AVO inversion for Lame parameters The target area is a Lower Cretaceous glauconite filled fluvial channel, deposited within an incised valley system. A 3D seismic survey was acquired in order to create a stratigraphic model, consistent with all available well control and matching the production history. The ultimate goal was to locate undeveloped potential within the gas sands. The field has been producing since the early 1980s and two of the earliest, most prolific producers have begun to water out. As the objective was stratigraphic in nature, the seismic data were processed with the objective of preserving relative amplitude relationships in the offset domain to allow for the use of AVO attribute analysis. AVO inversion for Lame parameters (λρ and µρ) has become a common practice as it serves to enhance identification of reservoir zones. Also, integration of AVO-derived attribute volumes with other non-AVO derived seismic attribute volumes can provide meaningful geologic information when tied back to well data and verified as correlating with rock properties. Computation of reservoir properties for determination of mathematical relationships between variables derived from well logs, for example, is usually done with non-linear multivariate determinant analysis using neural networks. This paper provides a case study of a 3D seismic survey in southern Alberta, Canada, where a probabilistic neural network solution was first employed on AVO attributes (Pruden, 2002, Chopra & Pruden, 2003). Using the gamma-ray, acoustic and bulk density log curves over the zone of interest, gamma-ray and bulk density inversions were derived from the 3D attribute volumes. This methodology was successful, in that two new drilling locations derived from this work encountered a new gas charged reservoir, that not only extended the life of the gas pool but added new reserves as well.

Multiattribute seismic analysis on AVO-derived parameters—A case study

Prospecting for reservoir zones in mature trends sometimes requires unconventional exploration tools. AVO has been successfully used as a direct hydrocarbon indicator in some clastic rocks. Lately, AVO inversion for Lame parameters (λρ and μρ) has been shown to enhance identification of reservoir zones. Furthermore, integration of AVO-derived attribute volumes with other non-AVO-derived seismic attribute volumes can provide meaningful geologic information when tied back to well data and verified as correlating with rock properties. This paper provides a case study of a 3D seismic survey in southern Alberta, Canada, where a probabilistic neural network solution was employed on AVO attributes. The results were integrated with other seismic attributes to develop a more comprehensive interpretation.

Multiattribute seismic analysis

Multiattribute seismic analysis is a broad term that encompasses all geostatistical methods that utilize more than one attribute to predict some physical property of the earth. Bivariate geostatistics is obviously the simplest subset of the multivariate techniques and thus the standard technique of cokriging could be called multivariate geostatistics. However, in this paper we will use the name to refer to geostatistical methods that use more than two variables. Even then, there are many different methods that fall under this heading. There are three major subcategories.