火成岩储层裂缝识别、预测理论与方法研究－以济阳坳陷火成岩油藏为例

Since 1970s, igneous reservoirs such as Shang741, Bin674 and Luol51 have been found in Jiyang depression, which are enrichment and heavy-producing. Showing good prospect of exploration and development, igneous reservoirs have been the main part of increasing reserves and production in Shengli oilfield. As fracture igneous reservoir being an extraordinary complex concealed reservoir and showing heavy heterogeneity in spatial distribution, the study of recognition, prediction, formation mechanism and the law of distribution of fracture is essential to develop the reservoir. Guided by multiple discipline theory such as sedimentology, geophysics, mineralogy, petroleum geology, structural geology and reservoir engineering, a set of theories and methods of recognition and prediction of fractured igneous rock reservoir are formed in this paper. Rock data, three-dimensional seismic data, log data, borehole log data, testing data and production data are combined in these methods by the means of computer. Based on the research of igneous rock petrography and reservoir formation mechanism, emphasized on the assessment and forecast of igneous rock reservoir, aimed at establishing a nonhomogeneity quantification model of fractured igneous rock reservoir, the creativity on the fracture recognition, prediction and formation mechanism are achieved. The research result is applied to Jiyang depression, suggestion of exploration and development for fractured igneous rock reservoir is supplied and some great achievement and favourable economic effect are achieved. The main achievements are gained as follows: 1. The main facies models of igneous rock reservoir in JiYang depression are summarized. Based on data and techniques of seism, well log and logging,started from the research of single well rock facies, proceeded by seismic and log facies research, from point to line and line to face, the regional igneous facies models are established. And hypabyssal intrusion allgovite facies model, explosion volcaniclastic rock facies model and overfall basaltic rocks facies model are the main facies models of igneous rock reservoir in JiYang depression. 2. Four nonhomogenous reservoir models of igneous reservoirs are established, which is the base of fracture prediction and recognition. According to characteristics of igneous petrology and spatial types of reservoir, igneous reservoirs of Jiyang depression are divided into four categories: fractured irruptive rock reservoir, fracture-pore thermocontact metamorphic rock and irruptive rock compound reservoir, pore volcanic debris cone reservoir and fracture-pore overfall basaltic rock reservoir. The spatial distribution of each model's reservoir has its features. And reservoirs can be divided into primary ones and secondary ones, whose mechanism of formation and laws of distribution are studied in this paper. 3. Eight geologic factors which dominate igneous reservoirs are presented. The eight geologic factors which dominates igneous reservoirs are igneous facies, epigenetic tectonics deformation, fracture motion, intensity of intrusive effect and adjoining-rock characters, thermo-contact metamorphic rock facies, specific volcano-tectonic position, magmatic cyclicity and epigenetic diagenetic evolution. The interaction of the eight factors forms the four types nonhomogenous reservoir models of igneous reservoirs in Jiyang depression. And igneous facies and fracture motion are the most important and primary factors. 4. Identification patterns of seismic, well log and logging facies of igneous rocks are established. Igneous rocks of Jiyang depression show typical reflecting features on seismic profile. Tabular reflection seismic facies, arc reflection seismic facies and hummocky or mushroom reflection seismic facies are the three main facies. Logging response features of basic basalt and diabase are shown as typical "three low and two high", which means low natural gamma value, low interval transit-time, low neutron porosity, high resistivity and high density. Volcaniclastic rocks show "two high and three low"-high neutron porosity, high interval transit-time, low density, low-resistance and low natural gamma value. Thermo-contact metamorphic rocks surrounding to diabase show "four high and two low" on log data, which is high natural gamma value, high self-potential anomaly, high neutron porosity, high interval transit-time and low density and low-resistance. Based on seismic, well log and logging data, spatial shape of Shang 741 igneous rock is described. 5. The methods of fracture prediction and recognition for fractured igneous reservoir are summarized. Adopting FMI image log and nuclear magnetic resonance log to quantitative analysis of fractured igneous reservoir and according to formation mechanism and shape of fracture, various fractures are recognized, such as high-angle fracture, low-angle fracture, vertical fracture, reticulated fracture, induced fracture, infilling fracture and corrosion vug. Shang 741 intrusive rock reservoir can be divided into pore-vug compound type, pore fracture type, micro-pore and micro-fracture type. Physical properties parameters of the reservoir are computed and single-well fracture model and reservoir parameters model are established. 6. Various comprehensive methods of fracture prediction and recognition for fractured igneous reservoir are put forward. Adopting three-element (igneous facies, fracture motion and rock bending) geologic comprehensive reservoir evaluation technique and deep-shallow unconventional laterolog constrained inversion technique, lateral prediction of fractured reservoir such as Shang 741 is taken and nonhomogeneity quantification models of reservoirs are established.

Direct Computation of 3D Shape Invariants and the Focus of Expansion

Structure from motion often refers to the computation of 3D structure from a matched sequence of images. However, a depth map of a surface is difficult to compute and may not be a good representation for storage and recognition. Given matched images, I will first show that the sign of the normal curvature in a given direction at a given point in the image can be computed from a simple difference of slopes of line-segments in one image. Using this result, local surface patches can be classified as convex, concave, parabolic (cylindrical), hyperbolic (saddle point) or planar. At the same time the translational component of the optical flow is obtained, from which the focus of expansion can be computed.

Vectorizing Face Images by Interpreting Shape and Texture Computations

The correspondence problem in computer vision is basically a matching task between two or more sets of features. In this paper, we introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. This representation has two components: (1) shape, or (x, y) feature locations, and (2) texture, defined as the image grey levels mapped onto the standard reference image. This paper explores an automatic technique for "vectorizing" face images. Our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. A hierarchical coarse-to-fine implementation is discussed, and applications are presented to the problems of facial feature detection and registration of two arbitrary faces.

Shape-Time Photography

We introduce a new method to describe, in a single image, changes in shape over time. We acquire both range and image information with a stationary stereo camera. From the pictures taken, we display a composite image consisting of the image data from the surface closest to the camera at every pixel. This reveals the 3-d relationships over time by easy-to-interpret occlusion relationships in the composite image. We call the composite a shape-time photograph. Small errors in depth measurements cause artifacts in the shape-time images. We correct most of these using a Markov network to estimate the most probable front surface, taking into account the depth measurements, their uncertainties, and layer continuity assumptions.

Properties and Applications of Shape Recipes

In low-level vision, the representation of scene properties such as shape, albedo, etc., are very high dimensional as they have to describe complicated structures. The approach proposed here is to let the image itself bear as much of the representational burden as possible. In many situations, scene and image are closely related and it is possible to find a functional relationship between them. The scene information can be represented in reference to the image where the functional specifies how to translate the image into the associated scene. We illustrate the use of this representation for encoding shape information. We show how this representation has appealing properties such as locality and slow variation across space and scale. These properties provide a way of improving shape estimates coming from other sources of information like stereo.

Shape Recipes: Scene Representations that Refer to the Image

The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (e.g., albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional representation, called a scene recipe, that relies on the image itself to describe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed shape from bandpassed image data. We describe the benefits of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.

Inferring 3D Structure with a Statistical Image-Based Shape Model

We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure'' model. The 3D shape of a class of objects may be represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes can then be estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We augment the shape model to incorporate structural features of interest; novel examples with missing structure parameters may then be reconstructed to obtain estimates of these parameters. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a dataset of thousands of pedestrian images generated from a synthetic model, we can perform accurate inference of the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

Virtual Visual Hulls: Example-Based 3D Shape Estimation from a Single Silhouette

Recovering a volumetric model of a person, car, or other object of interest from a single snapshot would be useful for many computer graphics applications. 3D model estimation in general is hard, and currently requires active sensors, multiple views, or integration over time. For a known object class, however, 3D shape can be successfully inferred from a single snapshot. We present a method for generating a ``virtual visual hull''-- an estimate of the 3D shape of an object from a known class, given a single silhouette observed from an unknown viewpoint. For a given class, a large database of multi-view silhouette examples from calibrated, though possibly varied, camera rigs are collected. To infer a novel single view input silhouette's virtual visual hull, we search for 3D shapes in the database which are most consistent with the observed contour. The input is matched to component single views of the multi-view training examples. A set of viewpoint-aligned virtual views are generated from the visual hulls corresponding to these examples. The 3D shape estimate for the input is then found by interpolating between the contours of these aligned views. When the underlying shape is ambiguous given a single view silhouette, we produce multiple visual hull hypotheses; if a sequence of input images is available, a dynamic programming approach is applied to find the maximum likelihood path through the feasible hypotheses over time. We show results of our algorithm on real and synthetic images of people.

Robust Photo-topography by Fusing Shape-from-Shading and Stereo

Methods for fusing two computer vision methods are discussed and several example algorithms are presented to illustrate the variational method of fusing algorithms. The example algorithms seek to determine planet topography given two images taken from two different locations with two different lighting conditions. The algorithms each employ assingle cost function that combines the computer vision methods of shape-from-shading and stereo in different ways. The algorithms are closely coupled and take into account all the constraints of the photo-topography problem. The algorithms are run on four synthetic test image sets of varying difficulty.

A Modern Differential Geometric Approach to Shape from Shading

How the visual system extracts shape information from a single grey-level image can be approached by examining how the information about shape is contained in the image. This technical report considers the characteristic equations derived by Horn as a dynamical system. Certain image critical points generate dynamical system critical points. The stable and unstable manifolds of these critical points correspond to convex and concave solution surfaces, giving more general existence and uniqueness results. A new kind of highly parallel, robust shape from shading algorithm is suggested on neighborhoods of these critical points. The information at bounding contours in the image is also analyzed.

The Role of Knowledge in Visual Shape Representation

This report shows how knowledge about the visual world can be built into a shape representation in the form of a descriptive vocabulary making explicit the important geometrical relationships comprising objects' shapes. Two computational tools are offered: (1) Shapestokens are placed on a Scale-Space Blackboard, (2) Dimensionality-reduction captures deformation classes in configurations of tokens. Knowledge lies in the token types and deformation classes tailored to the constraints and regularities ofparticular shape worlds. A hierarchical shape vocabulary has been implemented supporting several later visual tasks in the two-dimensional shape domain of the dorsal fins of fishes.

Shape from Contour

The problem of using image contours to infer the shapes and orientations of surfaces is treated as a problem of statistical estimation. The basis for solving this problem lies in an understanding of the geometry of contour formation, coupled with simple statistical models of the contour generating process. This approach is first applied to the special case of surfaces known to be planar. The distortion of contour shape imposed by projection is treated as a signal to be estimated, and variations of non-projective origin are treated as noise. The resulting method is then extended to the estimation of curved surfaces, and applied successfully to natural images. Next, the geometric treatment is further extended by relating countour curvature to surface curvature, using cast shadows as a model for contour generation. This geometric relation, combined with a statistical model, provides a measure of goodness-of-fit between a surface and an image contour. The goodness-of-fit measure is applied to the problem of establishing registration between an image and a surface model. Finally, the statistical estimation strategy is experimentally compared to human perception of orientation: human observers' judgements of tilt correspond closely to the estimates produced by the planar strategy.

Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects

We present the results of an implemented system for learning structural prototypes from grey-scale images. We show how to divide an object into subparts and how to encode the properties of these subparts and the relations between them. We discuss the importance of hierarchy and grouping in representing objects and show how a notion of visual similarities can be embedded in the description language. Finally we exhibit a learning algorithm that forms class models from the descriptions produced and uses these models to recognize new members of the class.