Catálogo de publicaciones - revistas

<jats:p> Reservoir stochastic simulation usually can be divided into two categories: object-based modeling method and pixel-based modeling method. For fluvial reservoirs, the complex geometry of sedimentary microfacies, such as the morphology of channels and natural levees, can be reproduced better by using the object-based method, but it also has difficulty in conditioning models to dense well data. Most of previous conditional methods are iterative optimization algorithms, which take a long time to reach convergence when there are many conditional data, and they generally get a low degree of conditionalization. Therefore, this paper suggests a new conditional methodology. The process of conditioning to well data is as follow: Firstly, the classical object-based method is used to establish the channel reservoir model. And on this basis, the distance parameter field (D1) related to the central lines of channels is established. After that, converting the information of channels and non-channels wells into the distance from the central lines, then set up the distance parameter field (D2) by Kriging and other methods. Finally, D1 is modified by using the logical relationship between D2 and D1, and then D1 is truncated to obtain the river channel model. The analysis of the results of three examples shows that this new approach can improve the conditional level greatly. </jats:p>

<jats:p> Carbonates commonly present a stacking-pattern architecture determined by the depositional processes despite the diagenetic modifications. This study integrates spectral cyclostratigraphy and petrophysical analysis to locate prolific reservoir beds to optimize reservoir development. It involves subdividing the reservoir succession into different hierarchical units to understand the variation of microfacies and pore-structure types. It proceeds by determining the variation-trends and discontinuity surfaces (chronostratigraphic and lithostratigraphic boundaries) by conjointly interpreting the prediction error filter analysis (PEFA) and integrated PEFA (INPEFA) and synthetic seismic trace and seismogram. We define the rock texture types (microfacies) through self-consistent approximation based on the INPEFA log while considering the variation of depositional sequence and grain size, core/thin-section petrography, and borehole electrical image interpretation. We establish lithology-depth and petrophysical classes-depth profiles using largely well-log data and propose new petrophysical classes for naturally fractured vuggy reservoirs. We predict porosity and permeability from the distribution of high-frequency cycles and lithofacies while considering the implications of diagenesis. The results show that the depositional processes and diagenesis largely control the quality of the reservoir beds in response to the relative sea-level variations. The reservoir beds comprise the reservoir units with connected-vugs- and fractures-dominated pore systems and horizons with microporosity-dominated pore systems that are candidates for secondary recovery processes. The study proposes a total porosity variation model that describes an External Cardiac Compression-like depth profile, showing alternation between low/tight and high porosity horizons, with the highly-porous intervals associated with the solution-enlarged porosity zones. Permeability varies with the pore type-mixing and rock texture type rather than the total porosity. The method well applies to distinguish flow conduits from baffles and barriers in complex carbonate reservoirs. </jats:p>

<jats:p> Nanopores are widely developed in organic kerogen in shale. Due to the large surface-bulk ratio, nanopores cause significant surface effects that affect the overall elastic properties of shale. It is essential to consider the effects of nanopores when applying pre-stack seismic inversion technique for shale reservoir prediction. Based on classical elastic theory, the technique of amplitude variation with offset (AVO) can usually extract velocity of longitudinal wave and shear wave, elastic modulus and density in shale reservoir from pre-stack seismic amplitudes, but often ignore the surface effects of nanopores. For the above reason, we derive a new AVO parameterization combining the nano-poroelasticity theory and AVO technique. The new derived parameterization contains four parameters (shear modulus of matrix, nanopores related parameter, shear modulus of saturated rock and density) which can be used to estimate the nanopore properties of shale. The model test and real seismic data examples verify the feasibility and suitability of the proposed method by applying Bayesian inversion technique with smooth background constraint. </jats:p>

<jats:p> Training image is a key input of multi-point geostatistical modeling. For modeling sedimentary facies under non-stationary conditions, it is common to first generate non-stationary training images, then use a partitioned simulation approach, and finally merge the realizations of each sub-region. We propose a new method for partitioning non-stationary training images based on features extracted using a deep network model. The basic idea of the method is to crop a training image with a sliding window to obtain the sub-blocks of the training image, and use the pre-trained convolutional neural network model as a fixed feature extractor for the sub-blocks. We use K-Means to cluster the extracted deep features and t-SNE to visualize the clustering effect, and assign the classification information of all feature points to the sub-blocks of the training image as its sub-region markers. Finally, we stitch the sub-blocks of the marked training images by position to obtain the partitioning results of the non-stationary training images. Experimental results show that the classification accuracy of the method reaches 90.53%, and the partition effect is relatively good. Research shows that the method can reproduce well the spatial variation characteristics of non-stationary training images, and provides a new method for processing multi-point geostatistical non-stationarity. </jats:p>

<jats:p> Since Miall’s architectural element analysis (AEA) was introduced to China in 1986, the method has been widely applied to studying the heterogeneity of oil and gas reservoirs in China. The development history, research field, research progress, and potential limitations of AEA in China have been reviewed based on 601 Chinese documents retrieved from the China National Knowledge Infrastructure (CNKI) database and some English documents retrieved from various databases. The development of sedimentary AEA in China experienced four stages: (1) the conceptual and introduction stage (1985–1995), (2) the test and trial stage (1995–2005), (3) the popularization and application stage (2005–2015), and (4) the innovative stage (2015 to present). The research field covers all reservoir types of continental petroliferous basins in China. Papers published on AEA were distributed in the fields of (1) alluvial fan reservoirs, (2) braided channel reservoirs, (3) meandering channel reservoirs, (4) fan delta reservoirs, (5) braided river delta reservoirs, (6) shallow-water delta reservoirs, (7) large-scale delta reservoirs, (8) beach and bar reservoirs, and (9) lacustrine deepwater gravity flow reservoirs. Among all these fields, the architecture of alluvial fans, braided channels, meandering channels, braided river delta, and fan delta reservoirs was mainly researched. AEAs in China have made outstanding innovations in hierarchical sedimentary boundary surface classification, depositional architectural models, and quantitative reservoir architectural modeling. Combining the hierarchical AEA with geostatistical modeling, big data, and artificial intelligence technology and comprehensively using geology, geophysics, and production information to realize the hierarchical integrated reservoir architectural characterization are the development directions of the AEA in China and around the world. </jats:p>

<jats:p> We have proposed a deep neural network-based framework for seismic facies classification. We implement two different neural networks based on the architectures of DeepLab v3+ and generative adversarial network for segmentation and compare the mapping results from seismic reflection data to lithologic facies. DeepLab v3+ predictions have sharper boundaries between the predicted facies whereas GAN output has a better continuity of predicted facies. We incorporate uncertainty analysis into the workflow using a Bayesian framework. The proposed approach consisting of joint analysis of predicted facies from multiple networks along with uncertainty in prediction accelerates the interpretation process by reducing the need for human intervention and also lessens individual biases that an interpreter may bring. We determine the effectiveness of the proposed algorithm by testing on field data examples, and we find that the proposed workflow classifies facies accurately. This may potentially enable the development of depositional environment maps in areas of low well density. </jats:p>

<jats:p> Understanding and modeling channels of high-pressure liquids during hydraulic fracturing and stimulated reservoir volume (SRV) estimation are critical for the evaluation of the fracturing effect and the prediction of oil and gas production in unconventional (low permeability and porosity) reservoirs. In this study, we developed a “focal mechanism-to-event” channel connection criterion based on the timing, location, and focal mechanism of microseismic events, enabling the channel to be perpendicular or parallel to the fracturing plane and more in line with the mechanical properties of rock rupture. We first evaluated the two endpoints of all channels as a set of spatial points, followed by the application of the alpha-shape method to detect the concave hull shape boundary formed by the spatial points. Finally, we calculated the SRV by combining the shapes of the concave hull and Delaunay triangulation. In the experimental simulation, we found that this method could effectively calculate the hull volume, and the detected concave hull included all microseismic events and channels. Ultimately, we applied the channel connection criterion and SRV estimation method to a stage of 3.18 h real fracturing data containing 216 microseismic events. The final calculation revealed a total of 410 connection channels and 4.3154 × 10<jats:sup>5</jats:sup> m<jats:sup>3</jats:sup> SRV, which is more consistent with the expected than the “event-to-event” or “event-to-channel” connection criteria and bin or convex hull SRV calculation methods. </jats:p>

<jats:p> The pore fluid distribution in nanosized pores in shale cannot be observed directly with the current observation equipment which makes it difficult to study the fluid behavior in the shale pore system. To better understand pore fluid evolution and distribution in shale and its effect on pore structure with increasing maturity from immature to oil windows, a serial of samples with different maturity were collected from the Lower Triassic lacustrine clay-rich Chang 7 shale in the Ordos Basin and were treated with oven drying and sequential extraction to remove water, mobile oil, and asphaltene. We measured the pore-size distributions in the presence of different pore fluids by using the mercury intrusion and N<jats:sub>2</jats:sub> adsorption and desorption testing. The results indicate that (1) the pores in the clay-rich samples are mostly slit-shaped mineral pores, consist of a small number of macropores (pore size &gt;50 nm) and a large number of mesopores (pore sizes between 7 and 36 nm), and also including a small amount of poorly developed organic matter-hosted pores. (2) Different types of pore fluids appear to occupy different pore-size ranges. Water prefers to occupy the mineral mesopores. Mobile oil (extracted by n-hexane) is mostly distributed in 2–10 nm-sized organic-matter-hosted mesopores. Asphaltenes are mostly distributed in mesopores in the range of 25–36 nm. (3) The presence of pore fluid significantly impacts the pore structures of shale. The pore volume and specific surface area decrease due to pore fluid occupation, even for low-maturity samples in which the organic matter pores also are developed but cannot be observed under SEM. (4) Organic matter abundance and thermal maturity have significant effects on the pore fluid composition and distribution. With increasing maturity, water is gradually displaced by oil due to hydrocarbon generation and accumulation in large mineral pores. </jats:p>

<jats:p> Research on marine-continental transitional shale in China is significant because this type of shale contains rich gas resources. The shale pore development, which is the key factor in determining the reservoir gas-bearing capacity, is complex. The organic matter (OM) content of the Shanxi and Taiyuan Formations shale in the southern North China Basin is medium to high, the mineral composition differs from that of other transitional shales, and the quartz content is higher than the clay content. The OM content and mineral composition exhibit a strong longitudinal heterogeneity. The pore types are mainly inorganic pores, and OM pores are less developed. The pore morphology is mainly parallel plate-like fissure pores, and there are a few ink-bottle-like pores composed of micro- and macropores. The specific surface area and total pore volume (TPV) of mesoporous-macropores are mainly controlled by mesoporous. The micropore volume is mainly controlled by micropores with sizes below 1.2 nm. Pore structure parameters and fractal dimension find that Shanxi and Taiyuan Formations shale has complex pore structure and rough pore surface. The fractal dimension D2 more accurately characterizes the roughness of the micropore surface. Compared with other shales, the micropore surface roughness of Shanxi and Taiyuan Formations shale is lower. The fractal dimensions and TPV do not correlate. The pore morphology and mineral characteristics of Shanxi and Taiyuan Formations shale leads to relatively low gas adsorption capacity and strong gas storage capacity, and is favorable for oil and gas migration. Due to the effects of compaction, macropores formed by quartz break and are filled with clay, resulting in a low daily gas production of the transitional shale in well MY1 after fracturing. The results provide references for the optimization of favorable reservoirs and the prediction of enrichment areas during transitional shale gas exploration. </jats:p>

<jats:p> “The present is the key to the past” is a foundational geologic concept that helps us contextualize buried subsurface features in current geologic analogs. As seismic interpreters, the generation of the geologic model should be unbiased, yet as humans our unconscious biases are expected, and we sometimes overlook anomalous reflection patterns in our seismic data that do not fit the model. As a result, we often disservice ourselves when we overlook these characteristics, potentially ignoring additional geologic context. These anomalous geoforms or funny-looking things (FLTs) may provide further geologic context and aid us in solving the geologic model if included. Crevasse splay on a continental slope marine environment is described and analyzed using attributes, seismic inversion, and voxel-based classification. We discuss possible causes that may have triggered the break of the levee on the Exmouth Basin during the Early Eocene and why a crevasse splay on a steep slope is an FLT. A possible explanation is that the presence of preexisting faults beneath said feature is the likely culprit for a levee break that created the crevasses splay. Thus, in contrast with the Led Zeppelin song about why the levee breaks, it is equally important to understand the preexisting faults when analyzing sediment supply. In addition, this highlights the importance of integral stratigraphic sequence interpretation — from deep to shallow — to understand geology in a full context. </jats:p><jats:sec><jats:title /><jats:p> Geologic feature: Crevasse splays and distributary channels Seismic appearance: Mounded high-amplitude reflection with hummocky low-amplitude internal reflection on vertical section, subparallel overlapping sinuous features on seismic attribute horizon slice Alternative interpretation: Mass-transport deposit, avulsion node; channel overbank Formation: The Wilcox Formation Age: Early Eocene-Late Paleocene Location: Offshore Western Australia Seismic data: Stybarrow 2008 M4D MSS obtained by the Geoscience Department of Australia Analysis tools: Coherence, multispectral coherency, principal curvature (K1 and K2), acoustic seismic inversion, flatness and curvedness, CNN automatic fault interpretation, and spectral decomposition </jats:p></jats:sec>