The study is based on multi well analysist drilled side by side in carbonate reservoir using high-resolution resistivity image. The objective is to define reservoir characterization, facies architecture, heterogeneity, and connectivity between two wells that is ready for reservoir modeling. The methods presented in this paper are using an automatic inversion and advanced algorithm to generate matrix conductivity images and curves, histogram, analyses rock texture heterogeneities, quantify fluid filled vugs density from high resolution borehole images, fast extraction of dips (beds, fractures), delineate planar features crossing deviated borehole over long distances, extract fracture traces and statistics. More than 3,000 picks of boundaries and fractures were found in a 3,300 ft horizontal length. Those divided into 6 different categories (Bed Boundary, Conductive Fracture, Discontinuous Conductive Fracture, Resistive Fracture, Litho-Bound Fracture, and Vugular fracture). Using high-definition imaging-while-drilling service provides supreme logging-while-drilling (LWD) imaging for reservoir description, from structural modeling, sedimentology analysis, image-based porosity determination and thin-bed analysis. The presence of heterogeneity in carbonates poses a challenge for the characterization of such rocks. The identification of textural variations advanced techniques in borehole image analysis have been applied and presented good results that determine secondary porosity and litho-facies, and, moreover, delivered new insight into previously established interpretations of the reservoir. The data comparison and validation to other measurement show a significant relationship to bring the value even beyond. By using an automatic inversion, the geological interpretation can be constantly delivered around the clock with higher consistency with the number of feature variation. It has been demonstrated that with the advanced analysis, microelectrical borehole images can provide quantitative measures of important reservoir parameters. Accuracy and consistency have been greatly improved since the introduction of microelectrical borehole image logging and subsequent automatic interpretation workflows.
Most conventional logging-while-drilling (LWD) tools acquire real-time measurements from a few inches to a few feet away from the wellbore for lithology, porosity, and saturation evaluation. Deep and ultra-deep resistivity tools were developed primarily for well placement and geomapping applications and can detect resistivity variations further away, in all directions around the wellbore. This article describes integration of deep and ultra-deep resistivity measurements for saturation mapping beyond the wellbore. Deep and ultra-deep resistivity tools can map multiple formation layers and determine the resistivity of those layers far from the wellbore. Since saturation is dependent on porosity and resistivity, a workflow has been developed to combine real-time shallow measurements with inverted LWD resistivity measurements and logging data from offset wells. The geological model is updated based on deep resistivity measurements, and petrophysical parameters for each layer, combined with inverted resistivity values, are used to derive saturation. Uncertainty is defined based on weighted uncertainty in porosity from offset wells and variation in resistivity across the mapped layers. Azimuthal resistivity measurements were also used to validate the inverted values and reduce uncertainties in cases of boundaries close to the wellbore. The workflow has been tested on several wells and different reservoirs. Several wells were presented in this study utilizing a combination of shallow and deep azimuthal data along with offset logs. Different layers of different formation characteristics and saturation profiles have been crossed and the data was utilized to validate the mapped saturation values compared to the nearby measurements. The resulting saturation profile provided critical information that enhanced understanding of the reservoir and gave better insight into variation in the far field. Such information is critical for completion-design and well-placement decisions and aligns with the industry's direction toward increased data analytics combining offset wells with real-time data. A continuous improvement of the model is expected as more wells are drilled and the database increases.
Conventional logging-while-drilling tools acquire measurements close to the wellbore. Recent development of deep and ultra-deep resistivity technology has enabled measurements more than 100 ft from the wellbore. Far-field petrophysics in simple carbonate lithology is possible since saturation is dependent on resistivity and porosity. Hence, combining deep and ultra-deep resistivity measurements with data from offset wells can provide reasonable saturation mapping. However, in clastics the evaluation is more complicated since resistivity is more dependent on lithology. In most cases, the main challenge in carbonates is permeability identification. Far-field carbonate petrophysics can be derived from mapped resistivity utilizing deep or ultra-deep resistivity and offset logs. Evaluation of far-field petrophysics away from the wellbore in clastics is more complicated as it requires lithology identification to be incorporated into the petrophysical model. A workflow has been developed integrating near-wellbore measurements with ultra-deep resistivity and anisotropy derived from 3D inversion. The workflow provides a qualitative indication of the lithology, allowing saturation in porous sand to be derived based on inverted resistivity. An innovative workflow has been developed based on the available data and combining multiple sources of data from near wellbore, offset logs to inverted resistivity data. 3D inversion plays a critical role in this workflow as it provides a lithology indication far away from the wellbore, differentiating between shale and sand. The identification of shale members at a distance also provides critical information for models of reservoir connectivity and potential barriers to water influx. The combination of far-field petrophysics with lithology gives better evaluation on a reservoir scale for critical decisions in terms of completion and reservoir performance. The new workflow has been successfully tested on several wells and different reservoirs. The workflow has been used for far field petrophysics and saturation mapping based on real-time logging-while-drilling (LWD) data and offset data analysis combining shallow and deep measurements with the 3D inversion from ultra-deep resistivity. There are several benefits from this innovative workflow, including optimizing well placement, improving understanding of fluid distribution, and optimizing completions design and reservoir management strategies.
The electromagnetic propagation (EMP) measurement frequently acquired with logging-while-drilling (LWD) tools in high-angle wells is sensitive to geometrical effects that can mask the true formation resistivity. Less commonly used, the LWD laterolog measurement is sometimes perceived as providing data too shallow to give a true formation resistivity (Rt). This paper presents modeling and actual examples to demonstrate that the laterolog can provide a superior resistivity measurement for formation evaluation than the does the EMP LWD tool. We examine the laterolog and EMP resistivities in several high-angle wells crossing carbonate formations in 8.5-in. and 6 1/8-in. hole sizes. In the 8.5-in. sections, producers and water injectors (high and low resistivity ranges) were evaluated. In the 6 1/8-in. sections, one reservoir sandwiched between two very high-resistivity layers and another borehole in a highly fractured reservoir were examined. The laterolog data were corrected for invasion using a 1D inversion of the memory data. Structure-based forward modeling was used to examine and explain the differences between the resistivity methods. In the first example, the laterolog data showed a clear conductive invasion profile. While the deepest laterolog real-time resistivity data indicated lower resistivity than the EMP resistivity, the true resistivity, Rt, from the 1D inversion matched the EMP resistivity. This result validated both measurements and emphasized that those differences were due to invasion. In the second example, a reservoir zone was initially drilled with resistivity measurements made only by the EMP tool. The LWD laterolog was run several days later, and the resistivity data were much lower in the relogged section compared with the EMP resistivity. The laterolog 1D inversion was unable to resolve Rt because of the excessively deep invasion that occurred over the course of several days. These two examples demonstrated that when acquired in normal drilling conditions, the laterolog measurements can provide the uninvaded formation resistivity even in the presence of invasion. A reservoir in another example was sandwiched between resistive layers that caused difficult-to-explain elevated EMP resistivity readings. Structural modeling reproduced the elevated behavior of the EMP data and explained the differences between resistivity measurements. This result showed that the laterolog is better suited to evaluate resistivity in thin reservoirs where there is a high-resistivity contrast to the adjacent layer. Finally, fractured reservoir examples are presented, which show that both the laterolog and EMP can be affected by the presence of fracture swarms. The examples presented in this paper demonstrate that in high-angle wells, under normal drilling conditions, invasion-corrected laterolog resistivity is nearer to Rt than is the EMP resistivity. Furthermore, the laterolog measurement provides data that are better input to water saturation calculations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.