Seismic Attributes Research Articles

Integrated characterization of deep karsted carbonates in the Tahe Oilfield, Tarim Basin

Abstract As transport channels of oil and gas, fracture networks can greatly improve reservoir seepage, which is of great significance to the hydraulic fracturing and hydrocarbon deposit exploitation in petroleum science and engineering. In this paper, our target reservoirs are deep karsted carbonates at depths of >6000 m and highly heterogeneous, leading to complex seismic responses with weak energy and low resolution. Therefore, it is challenging to predict the spatial distribution of a carbonate fracture-cavern reservoir and to characterize its delicate structure. We present a characterization method for an excellent fracture description by integrating several attribute results on 3D seismic field data. First, we use a noise elimination method to remove the noise interference in seismic data without damaging the fault structure characteristics. Next, we propose a novel spatially windowed 2D Hilbert transform-based operator to perform volumetric edge detection on 3D seismic field data. Then, the volumetric edge results are co-rendered with other seismic geometric attributes to generate multi-attribute fusion results for a comprehensive prediction that can excellently delineate geologic anomalies at different scales in deep carbonates. The results indicate that integrating several scale attributes can obtain more rich geological discontinuity and reveal more subtle fractures than using single attribute. The multi-attribute fusion results can effectively delineate some small-medium-sized faults, and they provide practical support for the exploration and production of Tahe carbonate fracture-cavern reservoirs.

Quantitative pressure and saturation engineering values from 4D PP and PS seismic data

AbstractFor field development and drilling decisions, production assets and reservoir engineers require dynamic reservoir properties, such as saturation and pressure changes of a reservoir from the pre‐production virgin state. To date, geophysicists have produced time‐lapse (4D) seismic attributes (mostly on stacked seismic data) rather than dynamic parameters directly. In this paper, we present a new method to estimate saturation and pressure properties from time‐lapse seismic data to provide to reservoir engineers. This new three‐step method is demonstrated over the Edvard Grieg field in the North Sea. We can realize this method thanks to advanced seismic multi‐component acquisition via PP and PS seismic data and processing that allows accurate estimation of amplitude‐variation‐with‐offset parameters P‐ and S‐impedances. With time‐lapse P‐ and S‐impedances optimally resolved, we estimate a stable set of axes identifying water saturation increase (water replacing oil), gas saturation increase (gas injection or gas out of solution), pressure increase (at injectors) and pressure decrease (at producers). Once these axes are obtained, we convert every 4D P‐ and S‐impedance data points into 4D pseudo‐saturation and pseudo‐pressure using a transformation of coordinates. We next use the rock physics relationships of this field to show that a linear relationship can be used to map any 4D change in the field from impedances to saturations and pressures. Key locations in the field with largest saturation and pressure changes are used to find calibration values at these extreme points. Next, from the pseudo‐seismic 4D data, a linear mapping is used to calculate actual reservoir property changes (fractions for saturation and bars for pressure). This allows us to obtain fieldwide dynamic values for water and gas saturations and injection and production related pressure changes. The results are shown, and dynamic changes are interpreted on the Edvard Grieg field data.

3D seismic analysis of mine planning using Aczel–Alsina aggregation operators based on T-spherical fuzzy information

3D seismic attributes analysis can help geologists and mine developers associate subsurface geological features, structures, faults, and ore bodies more precisely and accurately. The major influence of this application is to evaluate the usage of the 3D seismic attributes analysis in gold mine planning. For this, we evaluate the novel theory of complex T-spherical hesitant fuzzy (CTSHF) sets and their operational laws. Furthermore, we derive the CTSHF Aczel–Alsina weighted power averaging (CTSHFAAWPA) operator, CTSHF Aczel–Alsina ordered weighted power averaging (CTSHFAAOWPA) operator, CTSHF Aczel–Alsina weighted power geometric (CTSHFAAWPG) operator, and CTSHF Aczel–Alsina ordered.com weighted power geometric (CTSHFAAOWPG) operator. Some properties are also investigated for the above operators. Additionally, we evaluate the problems of 3D seismic attributes analysis to mine planning under the consideration of the proposed operators, for this, we illustrate the problem of the multi-attribute decision-making (MADM) technique for the above operators. Finally, we demonstrate some examples for making the comparison between prevailing and proposed information to improve the worth of the derived operators.

Examples of Paleokarst in Mesozoic Carbonate Formations in the Carpathian Foreland Area

A paleokarst system developed in the Upper Jurassic–Lower Cretaceous carbonate complex has been recognized in the Carpathian Foreland area. Well logs testing and core data as well as an acoustic imager, a microresistivity scanner and six-arm dipmeter images were used to identify and analyze the character of the paleokarst features. A detailed interpretation of microresistivity and acoustic image logs allowed for the identification of different types of karst forms, such as caverns; multidirectional fractures, including fractures widened by dissolution; and the type of sediments filling them. The analysis of the seismic survey was conducted by linking the paleokarst characteristic features recognized in the seismic image to the karst intervals determined from borehole data. The set of seismic attributes calculated from the analyzed 3D seismic data, including the RMS amplitude, instantaneous frequency, consistent dip, variance, sweetness and relative acoustic impedance, helped to delineate the zones of the paleokarst distribution. Within the interpreted paleokarst surface developed in the carbonate formations in the study area, there are sinkholes, limestone pavements and valleys. Furthermore, in the northwestern part of the analyzed area, the development of paleokarst forms is related to the presence of a relatively deep branch of a paleovalley formed in the Paleogene, as well as to numerous discontinuities developed in carbonate formations. The development of this type of larger karst form was probably controlled primarily by tectonic faults. The research conducted by the authors of this paper showed the widespread presence of paleokarst features in Upper Jurassic–Lower Cretaceous carbonate formations in the study area. A good spatial identification of the paleokarst surface can be important in a regional context, since the highest part of the profile of carbonate formations is the most important reservoir for geothermal or hydrocarbon resources in this region.

Reservoir porosity assessment and anomaly identification from seismic attributes using Gaussian process machine learning

AbstractPorosity, as one of the reservoir properties, is an important parameter to numerous studies, i.e., the reservoir’s oil/gas volume estimation or even the storage capacity measurement in the Carbon Capture Storage (CCS) project. However, an approach to estimate porosity using elastic property from the inversion propagates its error, affecting the result’s accuracy. On the other hand, direct estimation from seismic data is another approach to estimating porosity, but it poses a high non-linear problem. Thus, we propose the non-parametric machine learning approach, Gaussian Process (GP), which draws distribution over the function to solve the high non-linear problem between seismic data with porosity and quantify the prediction uncertainty simultaneously. With the help of Random Forest (RF) as the feature selection method, the GP predictions show excellent results in the blind test, a well that is completely removed from the training data, and comparison with other machine learning models. The uncertainty, standard deviation from GP prediction, can act as a quantitative evaluation of the prediction result. Moreover, we generate a new attribute based on the quartile of the standard deviation to delineate the anomaly zones. High anomaly zones are highlighted and associated with high porosity from GP and low inverted P-impedance from inversion results. Thus, applying the GP using seismic data shows its potential to characterize the reservoir property spatially, and the uncertainty offers insights into quantitative and qualitative evaluation for hydrocarbon exploration and development.

Integration of well logs and seismic attributes for prediction of thermal maturity and TOC content in the Kazhdumi Formation (central Persian Gulf basin)

This study proposes an indirect method for estimating source rock properties, specifically thermal maturity and Total Organic Carbon (TOC), using well logs and seismic data within the central Persian Gulf hydrocarbon fields. The primary focus is on the Kazhdumi Formation, an important source rock in the southern Iran. The study is carried out in two distinct steps, based on the employed data. In the first step, a Maturity Index (MI) is calculated utilizing well logs (such as neutron, density and water saturation) within the Kazhdumi Formation across six oil and gas fields. Then, the TOC content is determined using ∆Log R method across four wells. Geochemical data, including vitrinite reflectance and Rock-Eval pyrolysis data, used as validators of the calculated MI and TOC. In the second step, 2-dimensional post-stack seismic data analysis is conducted where the necessary data, including a full set of well logs are accessible. Multi-attribute regression is employed to extract seismic attributes and consequently used to predict the target parameters. MI and TOC logs are then predicted through Probabilistic Neural Network analysis (PNN) which is used for the indirect prediction of the target logs. Finally, two seismic volumes are obtained to provide a mapping of the predicted MI and TOC variations in the Kazhdumi Formation.One advantage of using this method is its efficiency and cost effective compared to the geochemical testing. Additionally, it provides a continuous prediction of the target parameter in an extensive area. Lastly, this method benefits from the availability of seismic data compared to the geochemical data in hydrocarbon fields that are typically sparse. This approach is proved to be applicable to source rock analysis in different areas for indirect estimation of thermal maturity and TOC.

Geologic causatives of the stratigraphic and structural heterogeneties of Baharyia formation, based on their seismic interpretation, Razzak field, Western Desert, Egypt

Evaluation of seismic attributes and seismic reflection characteristics analysis of Razzak Field was undertaken by subdividing the study area rock units. There are four seismo-facies units of varying parameters from top to bottom. These units are comparable to the Apollonia and Khoman Formations; the Abu-Roash Formation; Baharyia Formation and Alamein Formation. The seismo-facies unit 1 of the Apollonia and Khoman Formations reveal parallel to sub-parallel layering, with thickness increases northwards. The lithological distribution consists of limestone with minor clay intercalations with facies varying from middle neritic graded to outer neritic. Unit 2 of the Abu-Roash Formation shows divergent layering, with a thickness increasing gradually northwards and eastwards. The lithological distribution shows predominance of carbonate, sandstone occurring in minor portions and laterally graded into shale with littoral facies ranging to inner and middle neritic facies. Unit 3 of the Baharyia Formation reveals chaotic and oblique layering with a time thickness increases towards northeast and southwest parts, the lithological distribution exhibited sand and shale with minor limestone streaks, with facies varied changed from continental to inner-middle neritic, unit 4 of the Alamein Formation shows variation from parallel-subparallel orientation to oblique and chaotic with thickness increasing towards northwards and eastwards, the lithological distribution exhibited dolomitic limestone with minor streaks of limestone with facies varied from middle neritic facies to outer neritic and bathyal. Finally, the results are integrated to build up a seismo-stratigraphic model of the evaluated area of the northwestern Desert of Egypt. Seismic interpretation involves the construction of structure contour maps, in terms of time and depth, on the tops of the Apollonia, Abu-Roash, Baharyia and Alamein Formations. These maps show three structural closures, due to folding, that are dissected by NW–SE faults, analysis of relevant structural and stratigraphic seismic attributes such as root-mean-square amplitude, local structural dip, variance, ISO frequency component, sweetness and acoustic impedance average energy applied on reservoir tops, to enhance the visibility of faults, geological interpretation and the physical parameters of the subsurface related to lithology and stratigraphy for reservoir characterization. Finally, the results obtained are used to construct a seismic structural model. Integration of seismic and stratigraphic data is used to build a geological model of the northwestern Desert of Egypt.

Seismic geomorphological analysis of submarine fan architecture in the Baiyun Sag, Pearl River Mouth Basin: Impact of second-order relative sea-level change

Whereas the effects of high-order relative sea level change (3rd-order and higher) on the formation and architecture of submarine fan systems are relatively well established, a lack of large-scale spatial and temporal data has resulted in the impacts of low-order relative sea level change (i.e., 2nd-order) eluding characterization. With the aim of revealing the impacts of 2nd-order relative sea level, this study applied regional-scale seismic geomorphology to examine 142 Miocene-aged (21–8.2 Ma) submarine fans in the Baiyun Sag of the Pearl River Mouth Basin (China), integrating a large 3D seismic volume (6000 km2), well logs and core data. By extracting seismic attributes calibrated to well data, architectural elements were interpreted, statistically correlated, and their evolution through time was assessed. The results were as follows: (1) architectural elements of submarine channels and lobes, as well as associated highstand deltas, shelf-margin deltas and incised valleys, were identified based on their seismic geomorphological features and location, revealing that submarine channels shift paleo-seaward from incised valleys and feed lobes; (2) there is a positive correlation between average width of submarine channels and average area of lobes, as well as between the average width of incised valleys and average width of submarine channels; (3) the size of architectural elements is inversely related to proportion of sand; and most importantly, (4) the size variation of architectural elements coincides with changes in 2nd-order relative sea level. Results also indicate that rapid tectonic subsidence played an important control on the size and sandiness of deep-water architectural elements. The findings of this study may inform reconstruction of submarine fan systems and prediction of their petroleum reservoir potential in other systems globally.

Deep attributes: innovative LSTM-based seismic attributes

SUMMARY Seismic attributes are derived measures from seismic data that help characterize subsurface geological features and enhance the interpretation of subsurface structures: we propose to exploit the hidden layers of Long–Short Time Memory neural network predictions as possible new reflection seismic attributes. The idea is based on the inference process of a neural network, which in its hidden layers stores information related to different features embedded in the input data and which usually are not considered. Neural network applications typically ignore such intermediate steps because the main interest lies in the final output, which is considered as the exclusive exploitable feature of the process. On the contrary, here we analyse the possibility to exploit the intermediate prediction steps, hereafter referred as ‘deep attributes’ because they are produced by a deep learning algorithm, to highlight features and emphasize characteristics embedded in the data but neither recognizable by traditional interpretation, nor evident with classical attributes or multi-attribute approaches. Nowadays, classical signal attributes are numerous and used for different purposes; we here propose an original strategy to calculate attributes previously never exploited, which are potentially complementary or a good alternative to the classical ones. We tested the proposed procedure on synthetic and field 2-D and 3-D reflection seismic data sets to test and demonstrate the stability, affordability and versatility of the entire approach. Furthermore, we evaluated the performance of deep attributes on a 4-D seismic data set to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute.

Seismic attributes and spectral decomposition-based inverted porosity-constrained simulations for appraisal of shallow-marine lower-Cretaceous sequences of Miano gas field, Southern Pakistan

Seismic attributes can play an important role in the exploration of hydrocarbon-bearing stratigraphic systems. Incised valley systems are developed during the falling sea, which causes the deposition of coarse-grained sandstone facies inside the low-standing tracts (LST). These regional phenomena constrain the quantitative attributes of ultra-thin-bedded stratigraphic petroleum traps, e.g., vertical and lateral variations in the thickness, accommodation space, lithology, and porosity. This study deals with the application of the continuous wavelet transform (CWT) of a spectral decomposition (SD) tool on a 3D post-stack seismic volume of the Miano gas Field, Lower Indus basin, Pakistan. The results show that the CWT accurately detected the regionally faulted/fractured system and distinguished the frequency-dependent amplitude anomalies. The wedge model resolved a 24-meter-thick gas-bearing resource. Quality control analysis was carried out using CWT-based broadband processing between the designed amplitude spectrum of 17 Hz and 70 Hz. The reservoirs with over 25% porosity that were located within the shale-dominated facies with less than 8% porosity were imaged through the processing of the instantaneous spectral porosity model at the 48-Hz tuning block. Moreover, 190 to 165-m-thick thin-bedded sandstone reservoirs at a 25% porosity zone were resolved using 22-Hz and 28-Hz, which implicates the sea standstill and medium-to-coarse-grained depositional reservoir facies. The ultra-thin-bedded traps inside the laterally continuous stratigraphic lens of 121 m and the prograding clinoform lens of 101-m within the incised valley petroleum system were resolved using 48-Hz, which implicates the falling sea and fine-scaled transgressed erosional facies. These implications suggest that the identified regional stratigraphic traps have development potential for this gas field. The treatment of the inverted model at the highest frequencies can be utilized to investigate the porous stratigraphically trapped facies of LST and can serve as an important analogue for the leading gas field of the Indus Basin and similar basins.

Ensemble-based history matching of the Edvard Grieg field using 4D seismic data

The Edvard Grieg field is a highly complex and heterogeneous reservoir with an extensive fault structure and a mixture of sandstone, conglomerate, and shale. In this paper, we present a complete workflow for history matching the Edvard Grieg field using an ensemble smoother for Bayesian inference. An important aspect of the workflow is a methodology to check that the prior assumptions are suitable for assimilating the data, and procedures to verify that the posterior results are plausible and credible. We thoroughly describe several tools and visualization techniques for these purposes. Using these methods we show how to identify important parameters of the model. Furthermore, we utilize new compression methods for better handling large datasets. Simulating fluid flow and seismic response for reservoirs of this size and complexity requires high numerical resolution and accurate seismic models. We present a novel dual-model concept for a better representation of seismic data and attributes, that deploy different models for the underground depending on simulated properties. Results from history matching show that we can improve data matches for both production data and different seismic attributes. Updated parameters give new insight into the reservoir dynamics, and are calibrated to better represent water movement and pressure.

A novel fluid identification method based on a high-precision spectral decomposition method

Abstract Time-frequency decomposition technology is an effective tool for analyzing non-stationary signals. Improving resolution of spectral decomposition techniques is important to extract more useful information from the received signal. The Wigner-Ville distribution (WVD) has been widely applied in seismic signal analysis, it can better analyze seismic signals due to many excellent mathematical properties, but this method has a drawback that cross terms interference exists in the analyzing of multicomponent signals, which severely limits its application. The combination of the complex-domain matching pursuit (CDMP) with this approach effectively solves this problem. However, the conventional CDMP-WVD does not take the influence of the scale parameter on the Morlet wavelet waveform into account, which reduces the time-frequency resolution of CDMP-WVD. Therefore, to correct the defect that the atomic waveforms change only with the frequency parameter, we propose an improved spectral decomposition method ICDMP-WVD that considers the scale parameter. In this study, we first analyze influences of the scale parameter on Morlet wavelet waveform and make the scale parameter a search parameter that improves the computational efficiency and time-frequency resolution of the traditional CDMP-WVD method. Accordingly, the seismic dispersion-dependent attributes are calculated via combing the improved CDMP-WVD algorithm and the frequency-dependent AVO inversion. We adopt a two-step frequency-dependent AVO inversion method to improve the stability of the conventional frequency-dependent AVO inversion. Theoretical data and real data application show that the approach in this study can identify gas reservoirs efficiently and accurately.

Improving Reservoir Characterization and Prediction Via Machine Learning-Driven Integration of Subseismic Geologic Concepts, Geophysical Attributes, and Wells

Summary Reservoir dynamic performance is affected by subseismic (below the resolution of seismic imaging) geologic features such as submeter thin barriers and baffles, or high-permeability streaks. Here, we present an application of machine learning that has the potential to improve and significantly accelerate the workflow for generating reservoir realizations and scenarios with relevant subseismic features consistent with both well-log and 3D/4D seismic data and/or attributes. We leverage unsupervised machine learning methods, in particular, a modification of diffusion probabilistic modeling (DPM).

Seismic attribute transformation and porosity prediction of thin water‐rich sandstone based on Lambert W–R model

AbstractThe seismic attributes of water‐rich sandstone contain much information about the rock's physical properties and seismic wave parameters. They are commonly used to predict the rock's physical properties (e.g. porosity). However, the seismic attributes of water‐rich sandstone are affected by porosity, water saturation and thickness. To eliminate the influence of thickness on the porosity prediction of water‐rich sandstone and improve the accuracy of the porosity prediction, the authors propose a Lambert W–R transform method to isolate the contribution of thickness and porosity from seismic attributes. First, a rock physical model is used to calculate the equivalent elastic parameters of water‐rich sandstones with different porosity values and water saturation levels. Second, the seismic attribute dataset of water‐rich sandstone is established by forward modelling the seismic response of the wedge‐shaped water‐rich sandstone model, and the selection of sensitive physical properties is completed. Then, the transformation parameters (ζAhRs and ηAhRs) are obtained by Lambert W–R transformation, which is exponentially related to instantaneous amplitude. ζAhRs and ηAhRs are sensitive to thickness and porosity, respectively. Finally, an interpretative template for porosity prediction of water‐rich sandstone is established by cross‐plot analysis (ζAhRs and ηAhRs) and verified by a practical case. The verification results show that the porosity predicted by the interpretation template is consistent with drilling fluid consumption. However, it is lower than the porosity of logging constrained P‐wave impedance inversion.

Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data

Developing automatic history matching (AHM) methods to replace the traditional manual history matching (MHM) approach in adjusting the permeability distribution of the reservoir simulation model has been studied by many authors. Because permeability values need to be evaluated at hundreds of thousands of grid cells in a typical reservoir simulation model, it is necessary to apply a reparameterization technique to allow the optimization algorithms to be implemented with fewer variables. In basic reparameterization techniques including zonation and pilot point methods, the calibrations are usually based solely on the production data with no systematic link to the geological and geophysical data, and therefore, the obtained permeability distribution may be not geologically consistent. Several other reparameterization techniques have attempted to preserve geological consistency by incorporating 4D seismic data; however, these techniques cannot be applied to our fractured basement reservoirs (FBRs) as they do not have 4D seismic data. Taking into account these challenges, in this study, an AHM methodology and workflow have been developed using a new reparameterization technique. This approach attempts to minimize the potential for geological nonconsistency of the calibrated results by linking the permeability to geophysical data. The proposed methodology can be applied to fields with only traditional geophysical data (3D seismic and conventional well logs). In the proposed workflow, the spatial distributions of seismic attributes and geomechanical properties were calculated and estimated from 3D seismic data and well logs, respectively. After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. Then, the calibration of the permeability distribution is performed by adjustment of the ANN model. Modification of the ANN model is performed using the simultaneous perturbation stochastic approximation (SPSA) algorithm to calibrate transmission coefficients in the ANN model to minimize the discrepancy between the simulated results and observed data. The developed methodology is applied to calibrate the permeability distribution of a simulation model of Bach Ho FBR in Vietnam. The effectiveness of the methodology is evident by comparing the historical matches with an available manually history-matched simulation model. The application shows that the proposed methodology could be considered as a suitable practical approach for adjusting the permeability distribution for FBR reservoir simulation models.

Middle Miocene renewed subduction of arabian-eurasian plates: Implications for convergent tectonic mechanisms, tectonically-induced fluid overpressures, and sediment recycling dynamics

The complex interplay between thrust wedge deformation, tectonically-induced fluid overpressures, and the complex phases of wedge-top sedimentary depositional systems in active plate convergent margins presents a formidable challenge. The offshore Makran accretionary wedge (MAW) is the world's largest, formed by the convergence of Arabian plate underneath Eurasian plate in the Early Cretaceous, followed by Middle Miocene renewed subduction. In this research, geological and geophysical constraints derived from seismic and borehole stratigraphy, seismic attributes, depth structural maps, isopach maps and 3D structural geological models reveal that Middle Miocene renewed subduction controls thrust wedge deformation. Additionally, it influences deep fluid overpressure and the deformational pattern of wedge-top sedimentary depositional systems. The seismic data analysis suggested that the basement pre-kinematic Himalayan turbidities depositional system (HTDs) have been tectonically incorporated into the accretionary wedge by Middle Miocene renewed subduction, which gave rise to complex folding and N-dipping imbricate thrusting features. These N-dipping imbricate thrust faults initiated sediment underplating and served as major structural pathways for deep fluid overpressure migration, resulting in the formation of fluid escape pipes. The fluid escape pipes exhibit conical and bifurcating geometries with inclination angles of approximately 73°, 90°, and 100°, signifying the upward migration of deep overpressured fluids. Furthermore, the Middle Miocene to Pliocene syn-kinematic piggyback and growth depositional system (PGDs) show progressive thickening (Max ∼3600 m) towards onshore MAW. It lies unconformable above the pre-kinematic HTDs and carries valuable sedimentation records, growth stratal patterns, onlap terminations, and truncations against growing structures, revealed the timing and spatial deformation of thrust faults in 3D space. In contrast, the Pleistocene to Recent post-kinematic progradational depositional system (PDs) exhibits clinoforms marked by top-lapping, on-lapping, and down-lapping reflection, which are primarily controlled by sediment recycling dynamics and sea level change since Pleistocene. It shows progressive thickening (Max ∼1800m) towards the Makran subduction zone and lies unconformably above the syn-kinematic PGDs. Abridging the analyses, the tectonic reconstruction and deformation mechanisms model shows four phases, e.g. (1) initiation of subduction and accretionary wedge formation in Eocene, (2) N-dipping imbricate thrust faults and deep fluid overpressure triggered by Middle Miocene renewed subduction, (3) development of piggyback basins and growth stratal geometries in Middle Miocene to Pliocene, (4) thrust deformation ceases and development of progradational depositional system.

Seismic Attributes and Inverse Quantitative Static Density–Porosity–Constraint Reservoir Simulations of Naturally-Fractured Hydrocarbon-Bearing Lower Cretaceous Shallow-Marine Architectures, Onshore Indus, Pakistan

Seismic Attributes and Inverse Quantitative Static Density–Porosity–Constraint Reservoir Simulations of Naturally-Fractured Hydrocarbon-Bearing Lower Cretaceous Shallow-Marine Architectures, Onshore Indus, Pakistan

Deep carbonate reservoir characterization using multiseismic attributes: A comparison of unsupervised machine-learning approaches

Seismic reservoir characterization is of great interest for sweet spot identification, reservoir quality assessment, and geologic model building. The sparsity of the labeled samples often limits the application of supervised machine learning (ML) for seismic reservoir characterization. Unsupervised learning methods, in contrast, explore the internal structure of data and extract low-dimensional features of geologic interest from seismic data without the need for labels. We compare various unsupervised learning approaches, including the linear method of principal component analysis (PCA), the manifold learning methods of t-distributed stochastic neighbor embedding and uniform manifold approximation and projection (UMAP), and the convolutional autoencoder (CAE), on the 3D synthetic and field seismic data of a deep carbonate reservoir in southwest China. On the synthetic data, the low-dimensional features extracted by UMAP and CAE provide a better indication of porosity and gas saturation than traditional seismic attributes. In particular, UMAP better preserves the global structure of geologic features and indicates the potential of decoupling the gas saturation and porosity effects from seismic responses. We demonstrate that joint use of several types of seismic attributes, instead of using a single type of seismic attributes, can better delineate the reservoir structures using unsupervised ML. On the field seismic data, UMAP can effectively characterize the sedimentary facies distribution, which is consistent with the geologic understanding. Nevertheless, the porosity and saturation can not be reliably identified from field seismic data using unsupervised ML, which is likely caused by the complex pore structures in carbonates complicating the mapping relationship between seismic responses and reservoir parameters.

Quantitative identification and prediction of mixed lithology, Bohai Sea, China

The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.

Application of Seismic Attribute Analysis Technology Guided by Model Forward Modeling in L29 Area

Taking the SaErTU and Putaohua oil layers in the L29 well area of LHP oilfield in the northern Songliao Basin as the research object, In response to the difficulty of interference between thin interbedded sand and mudstone and strong reflection between strata, which have a significant impact on fine prediction of sand bodies, a stratigraphic model is established based on the geological characteristics of the target layer to eliminate the impact of stratigraphic reflection; Then add a thin layer of sand body to its interior, establish a thin interlayer model, and obtain a geological model that is more in line with the actual situation of the target layer. Using forward simulation, analyze the seismic response characteristics of sand bodies, extract 30 seismic attributes from 4 categories: amplitude statistics, composite seismic trace statistics, sequence and frequency spectrum statistics, and calculate the correlation between cumulative sandstone thickness and seismic attributes, and select a sensitive seismic attribute set;By combining sedimentary and drilling data, the seismic attribute with the highest sensitivity to the target layer is selected. The multi-level dimensionality reduction and gradual improvement of seismic attribute selection methods using “model forward modeling, attribute analysis, relevant optimization, and drilling implementation” can effectively improve the prediction accuracy of thin interbedded sand bodies and greatly reduce the risk of oilfield exploration and development.