Frequency bandwidth enhancement using 3D layered reflectivity inversion

Summary We propose an ensemble based seismic inversion framework to estimate static and dynamic reservoir parameters such as saturation, pressure and porosity fields using seismic data. The proposed method has certain novelties, in terms of the choice of seismic data, as well as uncertainty quantification of the estimates. Further, the method uses reservoir-engineering data as the prior information (such as pressure-saturation data) from reservoir simulation model to constrain the inversion process. Many conventional seismic inversion algorithms are deterministic in nature, and thus they pay less attention to uncertainty quantification. To quantify the uncertainties in the estimates, we adopt an iterative ensemble smoother as the inversion algorithm. Compared to the conventional deterministic inversion algorithms, this ensemble-based method is a derivative-free and non-intrusive approach, and has better capacity of uncertainty quantification. On the other hand, inverted seismic parameters, such as acoustic impedance, are often adopted as the data in inversion. In doing so, extra uncertainties may arise during the inversion processes. Here, we avoid such intermediate inversion processes by adopting amplitude versus angle (AVA) data. To handle the big-data problem in the AVA inversion process, we adopt a wavelet based sparse representation procedure ( Luo et al., 2016 ). Precisely, we apply a discrete wavelet transform to the AVA data, and estimate noise in the resulting wavelet coefficients. We then use the leading wavelet coefficients above a certain threshold value as the data in inversion. We apply the proposed framework to a 2D synthetic model for a proof-of-concept study. This reservoir model consists of three phases (water, oil and gas), and is a vertical section of a 3D Norne field model. We also test the performance of the framework in the 3D Brugge benchmark case that consists of two phases (water and oil). The numerical results from both cases indicate that the proposed framework can integrate the reservoir-engineering data as prior knowledge with seismic data, while achieving reasonably good estimates of both static and dynamic reservoir variables.

Seismic prospecting has been widely used in the exploration and development of underground geological resources, such as mineral products (e.x., coal, uranium deposit), oil and gas, groundwater, and so forth. Seismic impedance is a physical characteristic parameter of underground formation, which can be used in lithologic classification, rock characterization, stratigraphic correlation, and further mineral reservoir prediction, reserve estimation, and so forth. To estimate impedance from seismic data, one must perform reflectivity series inversion first. Under a simple exponential integration transformation, the reflectivity series can give the final estimated impedance. Sparse-spike seismic inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on reflectivity series. From sparse reflectivity series, the final estimated impedance has blocky features to make formation interfaces and geological edges precise, which is very important to accurately delineate the distribution range of mineral resources. The development of sparse-spike seismic inversion is still facing major challenges of fast optimization algorithms in real-life application, especially for massive seismic data in 3D case. Semismooth Newton algorithm (SNA), which is a second order mehtod and has super-linear, even quadratic convergence rate, is used to solve sparse-spike seismic inversion. The proposed algorithm has been compared with common used algorithms through a synthetic seismic trace and a 3D real seismic data volume. The results show that the proposed algorithm has faster convergence rate and fewer computation time. It provides a new effective algorithm to solve sparse-spike seismic inversion.

Summary Alluvial fans are widely developed in the mountain area T, western China . The coarse conglomerate masses in these alluvial fans cause high-velocity anomalies, which are difficult to identify with seismic data. These anomalies make building velocity models more difficult, and limit the ability to predict the locations of traps in the area. So identifying the alluvial fans and understanding the variation of inner lithology and lithofacies are very important for the exploration in the piedmont area. Traditionally, the sedimentary facies are interpreted based on the logging data from individual wells and seismic sequence knowledge, but it is difficult to identify the highvelocity conglomerate mass with seismic data, and to form the tie-well section only with the sedimentary facies interpretation from these wells. EM is a method that are sensitive to lithology variation, but it has low resolution along the vertical direction. In this paper, the EM and seismic methods are combined to interpret the lithofacies of Cenozoic formations and describe the sedimentary lithofacies with the reference of logging data. In the area T, the integration of EM and seismic data, as well as logging data, improves the precision of trap prediction and consequently decreases the drilling risk. The subsequent drilling figures prove the correctness of the lithology prediction, and the misfit between the interpreted depth of target layer and the real figure is decreased to less than 3%.

The prediction of thin limestone reservoir is an international problem in seismic exploration. The limestone reservoir of Da'anzhai member in the eastern Sichuan Basin features super tightness, strong heterogeneity and thin layers. Based on post-stack deterministic Constrained Sparse Spike Inversion, this paper applies geostatistical inversion to predict the thin limestone reservoir of Da'anzhai member. The geostatistical inversion method can effectively integrate geological, well logging and seismic data, taking advantage of the higher vertical resolution of well logging data and higher lateral resolution of seismic data, describing the subtle changes in the reservoir more accurately, thus it works well in identifying thin limestone reservoir with strong heterogeneity. Furthermore, the formation porosity is achieved by cosimulation, and the predicted results match well drilling result well.

Summary The seismic data spectral inversion method, arisen in recent years, can improve the resolution of seismic data for obtaining reservoir characteristics. It does not rely on prior information such as well-logging data. By using partial spectrum information from the input seismic trace data, a sparse reflectivity series can be inverted. The inverted reflectivity series can be used to improve the resolution and impedance inversion for thin layer reservoir prediction. In this paper, we implement seismic spectral inversion based on a compressed sensing algorithm, which can solve the ill-posed problem of the inversion process. In the process of inversion, we introduce a quadratic spectrum method to calculate the spectrum of seismic wavelet in the frequency domain, which can stabilize the inversion and handle real field data. Synthetic and real field seismic data examples show that the proposed spectral inversion based on compressed sensing algorithm is stable and effective. Moreover, the real field data inversion results show a good signal-to-noise ratio (SNR) and lateral continuity.

Valuable industrial oil and gas were discovered in the formations of Ordovician, Carboniferous and Triassic of the Tahe (塔河) oilfield, Xinjiang (新疆), China. The Carboniferous formations contain several oil- and gas-bearing layers. The lateral distribution of Carboniferous reservoir is unstable, and thin layers are crossbedded. This makes it difficult to do lateral formations’ contrast and reservoir prediction, so it is necessary to develop a method that can achieve reservoir lateral contrast and prediction by using multi-well logging data and seismic data. To achieve reservoir lateral contrast and prediction at the Carboniferous formations of the Tahe oilfield, processing and interpretation of logging data from a single well were done first. The processing and interpretation include log pretreatment, environmental correction and computation of reservoir’s parameters (porosity, clay content, water saturation, etc.). Based on the previous work, the data file of logging information of multi-well was formed, and then the lateral distribution pictures (2D and 3D pictures of log curves and reservoir parameters) can be drawn. Comparing multi-well’s logging information, seismic profiles and geological information (sedimentary sign), the reservoir of the Carboniferous in the Tahe oilfield can be contrasted and predicted laterally. The sand formation of Carboniferous can be subdivided. The results of reservoir contrast and prediction of the Carboniferous formations show that 2D and 3D pictures of multi-well reservoir parameters make the lateral distribution of reservoir and oil-bearing sand very clear, the connectedness of the reservoir of neighboring wells can be analyzed, and five sand bodies can be identified based on the reservoir’s lateral distribution, geological information and seismic data.

Geological structure model is the foundation of the sedimentary faces modeling, property modeling and digital simulation, which includes the horizon model and fault model. Fault model which is accorded with underground conditions plays an important role in the structure modeling, so it would be an important work to build the fault model perfectly with the seismic and logging data. This paper take the fault modeling of C84-6 well area in Chaoyanggou field as an example, building the fault model by four methods of ‘Projection of polygon’ ‘Correction of depth domain seismic body’ ‘Checking by overlooking’ ‘Correction of breakpoints’ by the software of Petrel. The fault model which is built by integrating logging and seismic data can be the skeleton to build the final geological structure model, and those methods mentioned in this paper has been applied to the modeling work in some adjacent areas at present.

To improve the accuracy of carbonate lithofacies modeling, mainly well data such as core, thin section and well logging data had been adopted in conventional methods. Although the reservoir types classification is very detailed, but it is usually difficult to integrate with seismic data to make 3D lithofacies model. To address the issue of carbonate lithofacies modeling, a new integrated carbonate lithofacies modeling technique was summarized based on thin section, core, well logging, 3D seismic data and production performance data. The integrated carbonate lithofacies modeling workflow mainly contains 5 steps. 1) Integrated lithofacies classification based on the core, thin section, well logging, FMI, CMR and production performance data. 2) Petrophysics lithofacies classification based on the cross-plot analysis between sensitive well log curves. 3) Petrophysics lithofacies prediction based on the sensitive well log curve by deep learning method, and verification by core lithofacies analysis. 4) Seismic inversion volume optimization by well lithofacies calibration. 5) Lithofacies modelling based seismic inversion based on the seismic inversion cut-off analysis (Fig.2). This workflow integrated seismic impedance (continuous variable) with lithofacies (discrete variable), and converts seismic inversion into lithofacies directly. According to the certification of new wells, this technique had been applied successfully in carbonate reservoir of M oil field in Middle East, it not only improves the accuracy of 1D lithofacies prediction for wells by deep learning method, but also improves the accuracy of 3D lithofacies modeling for the whole oilfield by well and seismic inversion integrated. The lithofacies modeling not only matched with lithofacies from core analysis and petrophysics lithofacies prediction from well log analysis, but also matched with seismic inversion data in no well area. The integrated carbonate lithofacies modeling workflow integrated thin section, core, well logging, 3D seismic data and production performance data, and improved the improves the accuracy of 3D lithofacies modeling for no well area. It’s useful for new wells optimization and high efficiency development with lower cost. The integrated carbonate lithofacies modeling workflow not only suit for carbonate reservoir, but also suit for clastic reservoir.

The same geophysical and geologic data have been interpreted for both unconventional shale-gas and conventional sandstone reservoirs simultaneously by framing and using a customized workflow. Customization was needed because of property differences between the two types of resources. Unconventional shale reservoirs differ significantly from conventional reservoirs in depositional environment, diagenesis, physical properties, and production techniques. Magnitudes and patterns of reservoir properties (e.g., velocity, resistivity, radioactivity, porosity, permeability, etc.) of shales differ significantly from those of sandstone/carbonate reservoirs. In a conventional play, we generally search for intervals with relatively low gamma, high resistivity, and low neutron porosity, whereas in shale gas we search for intervals with very high gamma, relatively higher resistivity than lean shales, low velocity, and very high neutron porosity. Production from shales is largely due to horizontal drilling, hydrofracturing, and stimulations. The shale-gas reservoir properties are governed mainly by organic richness and its thermal maturation. Organic material has significant impact on the elastic/petrophysical properties, which are manifested in log and seismic responses. Thus, identification and mapping of shale-gas reservoirs is based mainly on mapping of total organic carbon- (TOC) rich zones. The customized workflow was developed for identification of shale-dominant intervals and organic-rich-zones within them by integrating log and seismic data. Shales generally show low-amplitude signals, and internal reflection configurations are not easily identified. Seismic data conditioning and seismic attributes, e.g., phase and perigram, were applied for internal geometry mapping. Once shale-gas markers were identified, mapping of shale boundary, depth, thickness, and areal extent was done similarly to conventional plays. Sweet spots were identified by generating and analyzing log-property volumes like impedance, sonic, resistivity, and neutron porosity. Natural fractures, which are helpful in designing well trajectory, were mapped through geometric seismic attributes like curvature and dip azimuth. This study has enhanced geologic understanding and, hence, mappability of shale-gas plays along with conventional plays. The customized workflow is applicable in all stages of shale-gas-play exploration and development.

The application of the seismic reflection method to detect stratigraphic changes away from well locations is beginning to gain acceptance among Reservoir Engineers. Unfortunately one of the limitations of this method is the inherent low resolution of seismic data as compared to well log data. Several approaches have been proposed to produce well log-like or pseudo log traces from seismic reflection data. Although there is no improvement in the resolution, the pseudo log traces provide a more convenient way of visually corelating well data to seismic data. A new stratigraphic extrapolation method designed to overcome some of the drawbacks of low resolution is presented. This method consists of relating a model trace (initially derived from well information) to an actual seismic trace by varying the parameters of the model trace within well defined constraints until best match is achieved between them. Extrapolation of the stratigraphy away from the well is performed from trace to trace, the starting model for each trace being determined either from the final model for the previous trace or from an independently postulated model. An interactive computer program is employed for this purpose which allows the incorporation of additional information to the initial model. In this way the final stratigraphic model of the reservoir is the one which best matches the seismic data. Several examples showing the application of the method will be discussed.

The technique described is of utmost importance in stratigraphic interpretation because it measures sonic velocities and densities and their lateral variations. It will work on layers as thin as 40 feet. These changes can be indicative of facies changes or changes in lithology. The relative amplitudes and stacking velocities of seismic data comprise the required input to the method, however, if sonic and density logs from the area are available, this information" can be incorporated leading to greater control of the final results. The procedure requires that the shape of the propagating wavelet in the data be known and that it be a symmetric, zero-phase wavelet. The method is automated, and the end product is velocity and density information for a stacked seismic trace which is displayed similar to velocity and density logs. An iterative scheme minimizes the differences between the response of a seismic modeling system and the seismic data, based on matching amplitude ratios of the data. The method can be performed on as many stacked traces in an area as is necessary to achieve the objective of the explorationist. The technique has been applied to marine data from several different geologic provinces. When the results were compared to. sonic and density logs in the proximity of the seismic data, the correlations were good. INTRODUCTION In order to effectively use seismic data to define and evaluate potential stratigraphic traps, we must be able to detect lateral changes in velocity or some lithology-related physical parameter such as acoustic impedance. We will demonstrate two modeling methods which use the amplitude information in the data to transform seismic data into velocity or acoustic impedance logs. A related problem concerns the correlation of seismic data and well log information. The sonic and density logs give detailed information at the well which can be correlated to lithology; however, they can only measure velocity and density at the borehole. Conversely, seismic data, while lacking in the detail possessed by log data, can be used to detect and measure lateral variations in velocity. We will show methods which make seismic and well log data more correlative. We will further develop techniques for using the detailed velocity information available at the well to calibrate the logs derived from seismic data. These logs will then be used to measure velocities and their lateral variations away from the well. GENERAL SEISMIC PROCESS Figure 1 is a schematic representation of the seismic process and the role of the interpreter. In the center of the figure is a seismic trace which is the result of collecting seismic data over the subsurface depicted on the left of the figure. This operation can be expressed in terms of simple communications theory as the convolution of the earth reflection series with a propagating wavelet.

Research Article| June 01, 2006 Stratigraphy and lithologic heterogeneity in the Mannville Group (southeast Saskatchewan) defined by integrating 3-D seismic and log data Sabrina Sarzalejo; Sabrina Sarzalejo Department of Earth and Planetary Sciences, McGill University, Montreal, Quebec H3A 2A7, sabrinas@eps.mcgill.ca Search for other works by this author on: GSW Google Scholar Bruce S. Hart Bruce S. Hart Department of Earth and Planetary Sciences, McGill University, Montreal, Quebec H3A 2A7, hart@eps.mcgill.ca Search for other works by this author on: GSW Google Scholar Author and Article Information Sabrina Sarzalejo Department of Earth and Planetary Sciences, McGill University, Montreal, Quebec H3A 2A7, sabrinas@eps.mcgill.ca Bruce S. Hart Department of Earth and Planetary Sciences, McGill University, Montreal, Quebec H3A 2A7, hart@eps.mcgill.ca Publisher: Canadian Society of Petroleum Geologists Received: 10 May 2005 Accepted: 31 Mar 2006 First Online: 02 Mar 2017 Online ISSN: 2368-0261 Print ISSN: 0007-4802 © The Society of Canadian Petroleum Geologists Bulletin of Canadian Petroleum Geology (2006) 54 (2): 138–151. https://doi.org/10.2113/gscpgbull.54.2.138 Article history Received: 10 May 2005 Accepted: 31 Mar 2006 First Online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Sabrina Sarzalejo, Bruce S. Hart; Stratigraphy and lithologic heterogeneity in the Mannville Group (southeast Saskatchewan) defined by integrating 3-D seismic and log data. Bulletin of Canadian Petroleum Geology 2006;; 54 (2): 138–151. doi: https://doi.org/10.2113/gscpgbull.54.2.138 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of Canadian Petroleum Geology Search Advanced Search Abstract Wireline log and 3-D seismic data were integrated to define stratigraphic units and lithologic heterogeneity in the Mannville Group of southeast Saskatchewan. The Mannville in this area is a stratigraphically complex unit formed of fluvial to marine deposits that, although non-prospective for hydrocarbons in this area, share many similarities with time-equivalent strata in areas of heavy oil production. Seismic sequence stratigraphic principles permitted us to subdivide the Mannville into three packages. The ability to visualize the 3-D seismic data in a variety of ways, including arbitrary lines and stratal or horizon slicing techniques, helped us to define stratigraphic features that would affect fluid flow in hydrocarbon producing areas. These features include channels of various sizes and orientations, and channel elements such as scroll bars and lateral accretion surfaces, that are present within specific portions of the Group. Because the effectiveness of enhanced recovery methods such as steam-assisted gravity drainage depends on a development team’s ability to recognize reservoir heterogeneity, similar integration of 3-D seismic, well logs and other data types would assist greatly in developing the Mannville in heavy oil areas. There is close, but not one-to-one, correspondence between the stratigraphic units we defined and those established by regional log- and core-based correlations. Seismic resolution problems or log correlation styles could be responsible for the differences. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

Soft computing: tools for intelligent reservoir characterization (IRESC) and optimum well placement (OWP)

To improve the accuracy of carbonate reservoir property prediction, well data such as core, thin section and well logging data had been mainly adopted conventionally. Although the reservoir classification in term of property is determined, but it is generally difficult to realize 3D prediction integrating with seismic data. To address the problem of carbonate reservoir property prediction, a new technique was proposed based on thin section, porosity and permeability test data of core, well logging data, 3D seismic data and dynamic production data. It mainly contains 3 steps, 1) reservoir types subdivision on the basis of the core, thin section, well logging and production data, 2) the porosity-permeability relationship establishment for different reservoir types, and construction of the reservoir types identification curve based on core and well logging data, 3) reservoir type and porosity inversion constrained by seismic impedance, and reservoir property prediction volume estimation based on porosity and permeability relationship of different reservoir types. It breaks the bottleneck that is difficult to constrain rock type (discrete variable) using seismic impedance (continuous variable) directly, and constrain pore structure (continuous variable) related to rock type instead, and converts it into rock type using multi-parameters seismic inversion. According to the verification of new wells, this technique had been applied successfully in carbonate reservoir of F oil field in Middle East, it improves not only the accuracy of rock type prediction spatially, but also the accuracy of reservoir and permeability prediction.