Pre-stack Inversion Research Articles

Application of Facies Controlled Prestack Inversion Technology in Prediction of Deep Tight Gas in Continental Faulted Basin

It is difficult to predict tight sandstone gas in continental rift basin because of complex deep structure, fast transformation of sedimentary facies, large lateral variation of reservoir thickness, and tight and strong heterogeneity. Based on the petrophysical analysis of logging data, this paper improved the traditional low-frequency establishment method of prestack synchronous inversion which is under the constraints of fine isochronal sequence stratigraphy framework. The prestack AVO has attributed as covariate, and Bayes kriging method has applied to establish low-frequency model. Then, the relationship between density and P-wave and S-wave impedance has been introduced. The results show that the stability of density parameters has been improved, and the facies controlled prestack inversion with drilling verification has been realized which the number of layers with thickness error less than 20% accounts for 75.2%. Exploration practice shows that the facies controlled prestack inversion technology has important reference significance for other deep continental fault basins.

Improving the 3D facies model with the seismic-derived log volumes: a case study from the Asmari Formation in the Hendijan Field, southwest Iran

Abstract The prolific Oligocene-Miocene Asmari Formation is the primary reservoir rock hosting important hydrocarbon resources in Iran and consists of fluvial/deltaic siliciclastic and shallow marine carbonate rocks in the Hendijan Field. Due to the significant facies variability of the formation, the presence and quality of the reservoir pose a significant uncertainty in the characterization of the reservoir. This study compares two facies models, one based on well logs only and the second based on estimated facies volumes as a secondary variable in facies modelling. The petrophysical evaluation with microscopic thin sections and electrofacies analysis were used to classify the facies and determine the reservoir quality. As a result, the Ghar Member was identified as a highly porous interval, while the lower part of the Asmari Formation is characterized by tight facies. A sequential Gaussian simulation (SIS) algorithm was used to build the 3D facies model on the basis of the well logs. Acoustic impedance, shear impedance and density (derived from pre-stack inversion) were used as inputs to an artificial neural network to generate acoustic and density log volumes. Using electrofacies cut-offs, facies volume was constructed and used as a secondary variable to improve the initial facies model. The final facies model was compared with the blind well to check the validity of the prediction and satisfactory results were obtained. Since the values are present in all the cells of the reservoir (the traditional facies model only has values for the well location), the estimated facies volume is an accurate variable in the prediction of the facies model for the Asmari reservoir and for this reason the secondary facies model is more reliable than the primary one.

Shale gas geological “sweet spot” parameter prediction method and its application based on convolutional neural network

Parameters such as gas content (GAS), porosity (PHI) and total organic carbon (TOC) are key parameters that reveal the shale gas geological “sweet spot” of reservoirs. However, the lack of a three-dimensional high-precision prediction method is not conducive to large-scale exploration of shale gas. Although the parameter prediction accuracy based on well logging data is relatively high, it is only a single point longitudinal feature. On the basis of prestack inversion of reservoir information such as P-wave velocity and density, high-precision and large-scale “sweet spot” spatial distribution predictions can be realized. Based on the fast growing and widely used deep learning methods, a one-dimensional convolutional neural network (1D-CNN) “sweet spot” parameter prediction method is proposed in this paper. First, intersection analysis is carried out for various well logging information to determine the sensitive parameters of geological “sweet spot”. We propose a new standardized preprocessing method based on the characteristics of the well logging data. Then, a 1D-CNN framework is designed, which can meet the parameter prediction of both depth-domain well logging data and time-domain seismic data. Third, well logging data is used to train a high-precision and robust geological “sweet spot” prediction model. Finally, this method was applied to the WeiRong shale gas field in Sichuan Basin to achieve a high-precision prediction of geological “sweet spots” in the Wufeng–Longmaxi shale reservoir.

From Seismic Pre-Stack Elastic Attributes to Rock Properties. A Case Study in the Permian Basin, Onshore USA

The Permian Basin contains significant oil and gas-bearing shale deposits. Extending over 55 counties in West Texas and southeast New Mexico, it covers a vast region. It is the largest contributor to the oil shale boom in the US and in February 2022 accounted for more than 40% of US oil, Figure 1 (Federal Reserve Bank of Dallas 2022). Figure 2 displays the increase in produced oil from this basin from 2018 to 2022, Chevron News Room 2022. During the first years of production from these tight shales, seismic data weren’t considered to be essential for locating and drilling high-producing wells. However, the value of seismic data are now recognized and extensively used to design and execute lateral drilling programmes into the most prolific shale units. The shale deposits are widespread but highly heterogeneous in composition. Specifically notable is the lateral variability of Total Organic Content (TOC) and rock properties, such as porosity within the formations. In addition to varying shale properties, carbonate units were deposited simultaneously and are difficult to distinguish when only using reflectivity. Traditionally, the sonic and density values were inputted into supervised and non-supervised neural network applications to estimate rock properties. However, it is difficult to calculate density from conventional seismic data, and the results are typically questionable and have a high uncertainty element. To calculate density from seismic data, we need long offsets that are usable; most of the time, these data are not available (Aki, K., 1980). This study showcases a unique approach and its application using the LambdaRho-MuRho cross plot to estimate rock properties. The LambdaRho and MuRho values are derived from pre-stack inversions and are correlated to existing well data over the zone of interest. The results indicate that this is a more elegant and valid methodology, especially since it seems to produce a better, more accurate distinction between shale and carbonate domains.

Acquisition and Preparation of 3D Seismic Data for Pre-Stack Inversion at Siba Oilfield, Southeastern Iraq

The current study includes preparing a geometric proposal of the main parameters that must be worked within a seismic reflection survey to prepare a three-dimensional subsurface image. This image represents the Siba oil field located in Basra, southern Iraq. The results were based on two options for selecting these approved elements to create a three-dimensional image of the Mishrif, Zubair and Yamama formations as well as the Jurassic and Permian Khuff and the pre-Khuff reservoir area. The first option is represented in the geometry in option -1 is 12 lines, 6 shots, and 216 chs. The receiver density is 66.67 receivers / km2, so the shot density is the same. Total shots are 21000, which is the same number of receiving points. The survey area (in km2) and full fold area (in km2) are 317and 328, respectively. However, the second option in the geometry are16 lines, 16 shots, and 208 chs. Receiver density and shot density are 50, while the total shots are 16200 and the total receiving points are 16000; with survey area and full folded area are 198 and 198 km2. The aim of the current study is to characterize the structure and reservoir quality of Yamama and Zubair intervals and to image potential exploration targets such as the Mishrif, Jurassic, Permian Kuff and Pre Khuff formations. The present study succeeded in preparing the geometric proposal and getting at a subsurface image of the reservoir in three dimensions.

Using machine learning technology based on fuzzy neural network and geophysical data to predict the shale gas sweet spots: A case study of lower silurian longmaxi formation in Wy Block, Sichuan Basin

Compared with shale gas in north American, it is older, more mature, and significantly difference in China. Taking the Longmaxi Formation of WY block in Sichuan Basin as an example, the quantitative prediction method of sweet spots for high mature shale gas is formed. Firstly, the geophysical response characteristics of shale gas reservoir is studied, and the physical characteristics of the sweet spots are determined. Then, the distributions of reservoir evaluation parameters, such as total organic carbon, brittleness, and others, are obtained by pre-stack simultaneous inversion. Finally, fuzzy neural network method is carried out to predict the distribution of sweet spots and to deploy wells. Shale gas sweet spots in this block vertically concentrate within 30 m above the bottom of the Longmaxi Formation. And two grades of sweet spots are evaluated. The quantitative relationship between the production results of horizontal wells and the evaluation is established with Fuzzy neural network method.

Joint PP-PS seismic prestack inversion of thin-bed reservoirs

Abstract Thin beds are significant targets in seismic exploration of lithological reservoirs. The conventional Amplitude Versus Offset/Angle (AVO/AVA) inversion methods are based on Zoeppritz equations and their approximate formulas under single-interface assumptions, which are not suitable for thin-bed problems. Therefore, AVO/AVA inversion methods for thin-bed reservoirs are needed in high-resolution seismic exploration. Based on approximate reflection coefficients of a thin bed, this paper proposes a joint PP-PS prestack inversion method of thin-bed reservoirs using a least-squares approach. The method directly predicts elastic properties, including P-wave velocities, S-wave velocities and densities, as well as thin-bed thickness. In this study, four typical thin-bed models and an actual thin coal seam were used to prove applicability and superiority of the given inversion procedure. Compared to prestack inversion methods using exact Zoeppritz equations, our inversion method reveals thin-bed existence precisely, gives higher estimation accuracy and has better noise robustness. Future applications of the proposed inversion method are expected to advance seismic reflectivity techniques of thin-bed reservoirs, making it possible to detect potential source reservoirs with higher precision and accuracy.

Applicability Analysis of Pre-Stack Inversion in Carbonate Karst Reservoir

Although pre-stack inversion has been carried out on reservoir prediction, few studies have focused on the application of pre-stack for seismic inversion in fractured-cavity carbonate reservoirs. In carbonate rock, complicated combinations and fluid predictions in karst caves are remain unclear. Post-stack methods are commonly used to predict the position, size, and fillings of caves, but pre-stack inversion is seldom applied in carbonate karst reservoirs. This paper proposes a pre-stack inversion method for forward modeling data and oil survey seismic data, using both points to indicate the application of pre-stack inversion in karst caves. Considering influence of cave size, depth, and filler on prediction, three sets of models (different caves volume; different fillings velocity of caves; complicated combination of caves) are employed and inverted by pre-stack inversion. We analyze the pre-stack results to depict Ordovician oil bearing and characterize caves. Geological model parameters came from actual data of the Tahe oilfield, and seismic data were synthesized from geological models based on full-wave equation forward simulation. Moreover, a case study of pre-stack inversion from the Tahe area was employed. The study shows that, from both the forward modeling and the oil seismic data points of view, pre-stack inversion is applicable to carbonate karst reservoirs.

Fluid discrimination incorporating amplitude variation with angle inversion and squirt flow of the fluid

Pre-stack seismic inversion is an important method for fluid identification and reservoir characterization in exploration geophysics. In this study, an effective fluid factor is initially established based on Biot poroelastic theory, and a pre-stack seismic inversion method based on Bayesian framework is used to implement the fluid identification. Compared with conventional elastic parameters, fluid factors are more sensitive to oil and gas. However, the coupling effect between rock porosity and fluid content is not considered in conventional fluid factors, which may lead to fuzzy fluid identification results. In addition, existing fluid factors do not adequately consider the physical mechanisms of fluid content, such as squirt flow between cracks and pores. Therefore, we propose a squirt fluid factor (SFF) that minimizes the fluid and pore mixing effects and takes into account the squirt flow. On this basis, a novel P-wave reflection coefficient equation is derived, and the squirt fluid factor is estimated by amplitude variation with offset (AVO) inversion method. The new reflection coefficient equation has sufficient accuracy and can be utilized to estimate the parameters. The effectiveness and superiority of the proposed method in fluid identification are verified by the synthetic and field examples.

Seismic reservoir characterization and pressure depletion of the Abu Madi Formation in the NW Khilala field, onshore Nile Delta, Egypt

The Late Messinian Abu Madi Formation represents the most prospective deep reservoir target in the Nile Delta. Hydrocarbon exploration in the Nile Delta over the last few decades has been targeted to understand the Abu Madi reservoir quality and distribution. The integration of rock physics, amplitude versus angle (AVA), seismic interpretation, seismic attributes, and seismic inversion could successfully determine the characteristics of the late Messinian Abu Madi Formation in the North West Khilala (NWKh) field, which is located in the central onshore part of the Nile Delta, Egypt. The NWKh field is located on the eastern margin of the Disouq concession close to producing the West Khilala (WKh) field, with highly heterogeneous reservoir parameters. The reservoir is classified as lacustrine turbidites deposited in semi-isolated Late Messinian Basins. The Abu Madi Formation can be subdivided into upper and lower sand members. It is very common that they are separated by an intraformational shale layer. AVA analysis performed on the top of the Abu Madi reservoir suggested a class 2p. Seismic data were inverted to delineate the overall lateral extent of the reservoir. The prestack inversion process was integrated with rock physics modelling of the wells to generate high-resolution multiple rock property models to characterize the reservoir and observe or investigate different pressure depletion trends. The pressure depletion of the lower Abu Madi in the North West Khilala field was investigated laterally and vertically by the relative/full spectrum inversion and formation modular dynamic tester (MDT) pressure data, respectively. This depletion is due to communication with the West Khilala field, which was produced from the Lower Abu Madi (LAM) sand interval in 2007.

Nonlinear petrophysical amplitude variation with offset inversion with spatially variable pore aspect ratio

Petrophysical amplitude variation with offset (AVO) inversion, which combines a rock-physics model and prestack inversion, aims at effectively quantifying reservoir-property parameters from prestack seismic data. However, for a reservoir with complex pore structures, this approach may suffer from difficulties in rock-physics modeling, which hinders solving the relevant inverse problem. We have proposed a petrophysical AVO inversion method that considers the complex pore structures within reservoir rocks. The method integrates a differential effective medium model, the Gassmann equation, and the exact Zoeppritz equation as the forward operator. In particular, the pore complexity is addressed by favoring the spatial variation of pore type. Differing from conventional sequential inversion methods, the pore-type variation is introduced by updating the aspect ratio together with the reservoir parameters during the inversion process. To enhance the stability of the results, the estimation of aspect ratio and reservoir parameters is jointly conditioned to seismic and elastic data under a Bayesian framework, which leads to the objective function with joint data misfit terms. The objective function is optimized with a simulated annealing algorithm that includes the full nonlinearity of the forward operator. The proposed method is demonstrated by testing and applying on a carbonate reservoir. It generally outperforms methods that are either purely conditioned on the seismic data or based on a constant aspect ratio.

Petrophysical Properties of Tight Marl Reservoir and Its Influence on Fluid Percolation Capacity

Tight marl reservoir has always been a essential place for oil and gas storage in geophysical exploration. The oil under the Marl reservoir is a essential oil and gas storage resource in geophysical research, which accounts for more than half of all oil and gas resources. The marl reservoir has the characteristics of strong heterogeneity, complex lithology, and complex reservoir system, because most of them are buried deep underground, the research risk and cost are very high. In the past decade, most of the low-speed oil and gas resources developed in oil and gas fields have been exhausted, so the marl area has become an important distribution area of oil and gas research. This paper mainly studies the petrophysical properties of tight marl oil and gas reservoir and its influence on fluid percolation capacity. Through studying the characteristics of marl, it has the petrophysical properties of oil and gas reservoirs, pre-stack seismic inversion of shale oil and gas reservoirs, further exploration of natural resources, and its use to better benefit mankind. In this paper, the core sample basic parameter measurement, stress model, rock rheological force, petrophysical application method and related experiments are used to study the petrophysical properties of tight marl oil and gas reservoir and its influence on fluid percolation capacity, which is of great significance to the long-term stability of high arch dam, And the utilization and production of natural resources are of great practical engineering significance and social significance. The results show that the rock content varies about 5% in different rock physical properties, which shows different fluid percolation capacity. In the analysis of marl oil and gas reservoirs, it has important theoretical significance and engineering value to analyze the rheological effect of seepage field and stress field of dam bedrock under long-term load.

P-wave reflectivity parameterization and nonlinear inversion in terms of Young’s modulus and Poisson ratio

Young’s modulus and Poisson ratio are essential parameters for rock brittleness evaluation. At present, prestack inversions of these parameters mostly adopt linear approximation, whereas the nonlinear inversion methods that are based on the exact Zoeppritz equation are seldomly conducted. Nevertheless, in the case of large incident angles and high-impedance contrast, the nonlinear equation has higher accuracy than the linear approximation. It is more in line with the nature of geophysical inversion problems, and it describes the variation of formations’ elastic parameters better than the linear approximation does. Therefore, based on the exact solution of the Zoeppritz equation, we have parameterized and derived a new nonlinear reflection coefficient equation using nonlinear Young’s modulus, Poisson ratio, and density (YPD) as variables. Subsequently, by combining the inverse operator algorithm with the YPD equation, we develop a new nonlinear amplitude-variation-with-offset inversion scheme. This approach can robustly extract Young’s modulus and Poisson ratio from seismic data and identify shale-gas sweet spot locations within the reservoir, as well as predict reservoir fluid properties. We have tested the accuracy and applicability of this approach with synthetic and real data examples.

Reservoir characterization analysis in glacial reservoirs

This research aims to characterize reservoir properties by applying rock physics and AVO analysis followed by pre-stack inversion. Two approaches are investigated: One approach addresses the case in which there are wells and seismic data, and the other addresses cases where only seismic data are available. The former approach is achieved by using well-log cross-plots for rock physics modeling to determine the feasibility and pay zone through gas fluid substitution followed by AVO analysis. Pre-stack inversion is then used to predict porosity and gas saturation. In the second approach, a synthetic seismogram is generated and compared to the observed seismic trace at the location of interest by forward modeling P-wave interval velocity and density. The best-matching P-wave velocity and density are subsequently used to generate synthetic well logs at the same location. Pre-stack inversion is then performed on these synthetic wells to predict porosity and gas saturation. Property prediction is performed by a feasibility study and pay zone calculation using rock physics modeling of the nearest well to the seismic block. Finally, the results of this case are validated using real wells. This new approach of reservoir characterization using synthetic wells is applied on reservoir channels and yielded a fairly good porosity prediction but a less accurate prediction of gas saturation.

Cooperative multinetworks semi-supervised pre-stack seismic inversion

SUMMARY The elastic properties of the subsurface, such as density, P-velocity and S-velocity, can be estimated in pre-stack seismic inversion. In recent research, the deep neural network is widely used in pre-stack seismic inversion for its strong non-linear fitting and feature extraction ability. However, the label data is generally inadequate due to high drilling costs and strong data-sharing barriers in the field of exploration geophysics. In order to reduce the dependence of network performance on label data and ensure the accuracy of inversion mostly, semi-supervised learning is adopted. Here, we develop a cooperative multinetworks semi-supervised pre-stack seismic inversion method. In the cooperative multinetworks inversion framework, the inversion network, mapping network, and modification network are adopted to complete the inversion task cooperatively. A forward network is constructed to automatically generate seismic data from density, P-velocity and S-velocity, which can assist the above networks to complete semi-supervised learning. Compared with some published deep learning pre-stack inversion methods, the spatio-temporal correlation of data can be fully mined, the prior geological structure and low-frequency information can be utilized effectively, and reflectivity is adopted as an intermediate output parameter to improve the robustness of the method. The experiments on the Marmousi2 model demonstrate that cooperative multinetworks semi-supervised inversion strategy is superior to conventional semi-supervised inversion methods in both inversion accuracy and antinoise performance. In addition, the susceptibility experiments of the initial model indicate that the proposed method can maintain a high inversion accuracy with little effective information in the initial model. Finally, the proposed method is successfully applied to the field data and obtains a high-resolution inversion result.

Data-driven fast prestack structurally constrained inversion

Classic multitrace inversion, such as structurally constrained inversion (SCI), tends to arrange seismic data trace by trace to introduce constraints that are more in line with the structure or reflection features. However, this strategy of rearranging seismic traces extensively increases the number of calculations, thereby limiting the wide application of SCI in prestack inversion. We have developed two improved prestack SCI algorithms that can simultaneously invert all seismic traces with structural constraints in a short time and substantially reduce the dependence on seismic data quality. The key to the proposed techniques lies in achieving structural constraints by introducing a Hadamard product operator without rearranging the seismic traces because it avoids the generation of large-scale and memory-intensive convolution matrices and structural constraint operators. We have deduced a corresponding fast algorithm and named it fast structurally constrained inversion. In addition because the quality of seismic data plays a decisive role in inversion, we further have developed a data-driven fast structurally constrained inversion (DFSCI) algorithm wherein a local crosscorrelation coefficient reflects the reliability of the local seismic data to a certain extent. Therefore, applying DFSCI to control the contribution of seismic data at each sampling point in the inversion can reduce the dependence of seismic data quality. The noise resistance capability and the spatial continuity of the proposed methods have been verified through numerical experiments and a field data example.

Lithofacies and fluid prediction of a sandstone reservoir using pre-stack inversion and non-parametric statistical classification: A case study

Lithofacies and fluid prediction of a sandstone reservoir using pre-stack inversion and non-parametric statistical classification: A case study

Multi-parameter pre-stack seismic inversion based on deep learning with sparse reflection coefficient constraints

In the field of seismic inversion, Convolutional Neural Network (CNN) has been extensively applied for their powerful capability of feature extraction and nonlinear fitting. However, the insufficient amount of labeled seismic inversion dataset impedes the application of CNN in seismic elastic inversion. Besides, the lack of effective geophysical constraints in conventional CNN will make the network prone to over-fitting, leading to unstable inversion results. In this paper, a workflow is developed for generating sufficient and diverse datasets for pre-stack seismic inversion with limited log and seismic data. The Sequential Gaussian Co-Simulation algorithm is used to simulate the changes in the reservoir space under the constraints of the low-frequency model. At the same time, the Elastic Distortion algorithm is used to simulate the complex geological structures. This can increase the diversity of the strata longitudinal combination by enriching the combination mode of stratigraphic parameters. Besides, the combination of a U-net and three fully connected networks (UCNN) is proposed to predict the elastic parameters from seismic data. In UCNN, the sparse reflection coefficient is used as a constraint to improve the accuracy of the network. The performance of this method was evaluated by synthetic and field data examples. The results show not only the effectiveness of the proposed method but also demonstrate its outperformance over the conventional deep learning method. The R2 scores of density, Vp and Vs are 0.94, 0.98, 0.98.

Deconvolución mediante filtro extendido de Kalman para la rigurosa extracción de la reflectividad sísmica

Deconvolution attempts compensating for the distortions affecting a recorded seismogram, increasing its bandwidth and extracting subsurface reflectivity from such seismic trace. The estimated reflectivity needs the highest reliability and resolution because of its subsequent use in the pre-stack seismic processing sequence and seismic inversion. We implemented the predictive deconvolution algorithms, the homomorphic Phase Inversion, and the Extended Kalman Filtering. Their application to synthetic traces extracted reflectivity whose comparison with well-bore allowed comparing the reliability between methods. The algorithms applied to an offshore record provided results whose comparison permitted to analyze the impact of the deconvolution assumptions on each method performance.

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.