We present a novel methodology for integration of high-angle/horizontal (HA/HZ) well data into 3D geomodels as a natural extension to well placement workflows. Log interpretation, typically done in 2D cross-sections, is based on 1-D automated inversion, yielding near-wellbore reservoir structure and properties. 2D cross-section, updated by inversion and further refined using 2D/3D modeling, is subsequently retrofitted into the geomodel so as to minimally perturb the original topology. Changes in positions/dips/azimuths of boundaries and faults, cell properties, and further local grid refinements are applied automatically. The updated geomodel honors high-resolution logs and low-resolution seismic/nearby wells data. We demonstrate this on a typical real-time well placement scenario. Electromagnetic log interpretation codes are integrated as a high performance computing (HPC) Web service into a geosteering/model update workflow. As the initial model, we use 3D geomodel constructed from seismic/vertical well data. A "curtain" cross-section is extracted, edited based on 1D inversion, and further refined to match HA/HZ logs through 2D and 3D forward modeling, by changing properties/dips/layer thicknesses /fault positions, while preserving the original topology. Then, updated node coordinates and cell properties of the affected pillar grid region are calculated to optimally retrofit the changed 2D cross-section into the grid. These changes are then automatically applied to the 3D model. For quality control, we recompute the 2D cross-section from the refined geomodel. Ultimately, we arrive at the geomodel that honors both seismic and resistivity well-log data. The combination, in a single workflow, of physics-based log modeling codes, Services-Oriented Architecture, HPC framework, and the solver to optimally retrofit 2D cross-sections into 3D models, creates a qualitatively new opportunity for well placement engineers. This integrated workflow (1) maximizes the value of deep directional resistivity well-logs and real-time well placement interpretation by incorporating them into the source of data for building geomodels; (2) radically speeds up the model refinement loop by automatically calculating and applying the modifications to 3D reservoir model; (3) enables geoscientists to directly refine geomodels while geosteering. The latter has not been a standard practice, hindered by challenges of scale difference between geomodels and well-logs and lack of availability of efficient modeling codes.
Log simulation is critical for understanding and interpreting logging tool responses. It helps the log analyst understand near-wellbore measurements in complex environments, and particularly in anomalous situations. Simulated logs can also be used in "what-if" scenarios or as part of an iterative scheme to invert for the formation's geometry and material properties. We describe our implementation of a forward modeling and inversion environment with a multitier, Web-based architecture. The Web platform offers universal access from the user's desktop through a common Web browser to the simulator engine, running on a high-performance compute server. The application also allows the user to invert for near-wellbore rock properties from a set of logs. In addition to user-accessible Web pages for interactive use of the application, a programmatically accessible log simulation Web Service is created. Regardless of the host platform, this allows any networked application to access the simulator library without the need to replicate code, thus facilitating the development of formation evaluation applications. Because the library is hosted on a high-performance cluster (HPC), the computational engine always runs in the shortest possible time. Introduction The essence of log interpretation is the solution of the inverse problem; that is, the determination of formation parameters from logging data. This task becomes difficult when the logging environment departs from simple well-understood geometries. Many tools are designed to make deep-reading measurements, and therefore are susceptible to formation heterogeneities. The most common environmental effects are formation dip, invasion of borehole fluids, and the influence of neighboring beds. Frequently, a combination of these effects can obscure the true formation properties (e.g., formation resistivity and porosity), making it necessary to construct a complex 3D model of the formation to obtain an accurate solution. The only way to validate these complex models is by tool response simulation or inversion. Some logging tools (such as dual induction and dual laterolog) do not provide sufficient information to perform accurate inversion. Iterative forward modeling of what-if scenarios is routinely used in these cases. Iterative modeling simulates the exact tool response in a series of given formations. However, this process requires a high level of user interaction. The initial formation model must be constructed from logs, cores and geological information, and then updated manually. A solution is reached when a computed log matches the field log. Automated inversion is more efficient because no interaction is required after the simulation is launched. During the past decade, the introduction of array measurements with greater information content has made it possible to routinely use inversion in log interpretation. In fact, some modern tools record so much data that interpreting their response without the use of inversion presents a considerable challenge. Our interactive Web-based interface provides a convenient and universal user access to the forward modeling and inversion software, while the Web-based programming interface to the simulator makes it available as a library to other oil and gas applications. Fundamentals of 3D Modeling and Inversion Electromagnetic and sonic tool modeling in 3D geometries is performed primarily by using finite difference (FD) or finite element (FE) methods. These codes solve partial differential equations in terms of a large number of simultaneous linear equations. The equations are solved by matrix methods to yield simulated tool response at discrete points in space. Here, we use the finite difference method to model resistivity1,2 and sonic3 tool response. The finite difference discretization of a problem leads to a regular grid representation. Although this grid usually does not conform to the formation geometry, it can be made to approximate any geometry by using material averaging techniques. The matrix equations resulting from the finite difference discretization are usually well structured because of the regularity of the grid, and they are always sparse because the derivatives are approximated locally. Thus, the equations can be easily solved by fast, specialized computational methods.
Optimal hydrocarbon production is facilitated by a continual adjustment of wellbore pressure or flow rate, most easily implemented with an automated control system. For each well, this system consists of a tuned logic controller, a remotely operable throttle valve, and sensors such as pressure transducers and/or flow meters. Joint modeling of the reservoir and the control components is necessary for tuning the feedback algorithm in order to have a stable and acceptable rate of response (SPE 84219). Given the target audience, a platform independent user-friendly front-end is desirable; but the system evaluation is computationally intensive, thus necessitating a sophisticated back-end server. To this end, we present a multi-tier Web application that offers a universal interface to a simulator running on a central compute server. The rendering of the Web interface is driven by the data. The application delivers the simulation input data to the compute server through a relational database. The results may be examined online and parameters adjusted until a satisfactory step response is obtained. The method is also applicable for downloading tuned parameters to a semi-autonomous wellsite controller. Introduction Smart completion systems may be operated by continuous or periodic throttling of downhole valve assemblies.1,2 By adjusting the resistance to flow from various formation intervals, production may be "controlled." Various objectives may be desired, but the primary one is likely to be the reduction of water to oil ratio (WOR). A decision to install a smart completion to achieve this objective will be dictated by economics - a cost/benefit issue. Oil production in relation to water may be enhanced by altering sweep, i.e., adjusting injection or production rate ratios of patterned wells, or by changing vertical contact of the invading fluid by throttling sections within a well. It has been argued that the former is principally a flow control requirement, whereas the latter is exercised primarily as a pressure control mechanism.3 Regardless of which mechanism is implemented, it is expected that an optimal production plan4,5 would dictate a flow rate or a pressure for injection and production at the smart wells. A system to implement either of the control mechanisms requires appropriate sensor measurements, which upon comparison with the desired value will generate a deviation or an error. Both the instantaneous and the historical values of the error signal may be used by a control algorithm to drive a smart completion such that the error is reduced to arbitrarily small values. Controllers consisting of a Proportional, Integral and Derivative (PID) components, or their digital versions, function in the above mode.6 A block diagram illustrating the feedback system is given in Fig. 1. A significant aspect of controller analysis is the design of a stable but responsive controller algorithm. For example, in a PID system, the magnitude of the constants driving each of the components is "tuned" to achieve an acceptable response. Various techniques for carrying this out are well known,6 a number of which are applicable to linear, drift-free systems.
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