Successful in-situ fluid cleanup and sampling operations are commonly driven by a fast and reliable analysis of pressure, rate, and contamination measurements. Currently, techniques such as pressure transient analysis (PTA) and rate transient analysis (RTA) provide important information to quantify reservoir complexity, whereas fluid contamination measurements are overlooked for reservoir characterization purposes. The objective in this paper is to introduce a new interpretation technique to relate fluid contamination measurements with reservoir properties by identifying early- and late-time flow regimes in the derivative plots of reciprocal fluid contamination. Among several applications, this new transient analysis method is effective for improving logging-while-drilling (LWD) fluid sampling operations. The derivative methods used in PTA and RTA inspired the development of the new fluid contamination interpretation method. Contamination transient analysis (CTA) evaluates transient measurements acquired during mud-filtrate invasion cleanup to infer reservoir geometry. We apply derivative methods to the reciprocal of the time evolution of fluid contamination to identify flow regimes in cases of water-based mud invading either water-or hydrocarbon-saturated formations. LWD operations are considered under a continuous invasion effect, i.e. the fluid cleanup procedure is performed while mud filtrate continues to invade the formation. This constraint brings about a significant technical challenge for LWD fluid sampling jobs. Alternatively, this new method could be integrated with other pressure transient techniques to improve the interpretation of measurements. For example, in a pretest case where the pressure transient does not achieve the radial flow regime, fluid cleanup could provide complementary information about late-time flow regimes to enhance the acquisition of measurements in real time. We document synthetic and field examples of applications of a new interpretation method. Seven reservoir cases are simulated to obtain contamination data: (1) homogeneous isotropic reservoir, (2) formation thickness, (3) laminated formations, (4) geological faults, (5) mud-filtrate invasion (6) reservoir properties, and (7) permeability anisotropy. All these cases are compared for single-phase and multiphase flow during LWD fluid sampling operations. Additionally, field case studies are analyzed to highlight the value of the reciprocal contamination derivative (RCD) in real-time operations. Reservoir limits and features such as saturating fluid and depth of invasion are identified in the flow regimes detected with derivative plots of the reciprocal of the contamination. Consequently, LWD cleanup and sampling efficiency could be optimized based on contamination transient analysis by identifying the flow regimes taking place in the reservoir during filtrate cleanup, hence improving the prediction of the time required to acquire non-contaminated fluid samples. The new approach of the reciprocal contamination derivative is an alternative way to optimize fluid cleanup efficiency and to quantify the spatial complexity of the reservoir during real-time LWD operations. In addition, this new technique enables the evaluation of reservoir properties in less operational time than PTA without the need of pressure build-up stages, increasing fluid sampling efficiency in terms of quality and time.
A mature oil field reservoir is characterized by gathering the geological and petrophysical data with the dynamic data. The product allows the defining of vertical reservoir units and their continuity along the field. Reservoir properties, seals and barriers are determined by pressure data and compared with the petrophysical interpretation.This document presents a profound study on pressure data interpretation and how to integrate this information with a petrophysical-geological model obtained by well log data analysis. The sands correlation, reservoir properties, faults location and vertical seals, are interpreted at first with the well log data (GR-SP-RES-DN-PE) and seismic data. Then the pressure data points are analyzed taking into account the previous interpretation to confirm the sands vertical connectivity, fluid gradients and the fluid contacts. Additionally, the mini PBU transients are diagnosed by means of computer-aided type curves to evaluate flow regimes in the near wellbore region and to calculate the permeability. Finally, a fully integrated static petrophysical model is built based on the previous study.The geological cross section is compared before and after the pressure data integration. Ten recently drilled wells from three different reservoir areas with pressure data available are analyzed and presented in this paper. The pressure data evaluation allows identifying the 4 different formation units and its water oil contact (WOC), according to the pressure gradients. The pressure behavior along the horizontal section permits to estimate the sands continuity and the connectivity between three areas. The static reservoir pressure analysis serves to validate and improve the basic well log interpretation and should be a part of the formation evaluation workflow. In addition, a near wellbore model is a strong tool to support and improve the type curve diagnose and interpretation of dynamic data.This paper provides a great insight on reservoir characterization and the benefits of the pressure logging tool for gathering static and dynamic information into geological models. In many cases formation testing data interpretation is based solely on the spherical flow analytical model. This straightforward approach might bias some features of the near wellbore region, which can only be assessed by pressure transient diagnosis (e.g. pressure derivative). Modeling of formation pressure testing as an interpretation tool strongly improves the near wellbore region dynamic description.
Reservoir Fluid Geodynamics (RFG) is a novel thermodynamic methodology that integrates pressure-volume-temperature (PVT), geochemical fingerprinting (GCFP) and reservoir geology with downhole fluid analysis (DFA) data to understand the evolution of reservoir fluids over geologic time. RFG enables the enhancement of reservoir description, estimation of reservoir fluid properties, and optimization of data acquisition plans. Deep-water reservoirs comprise multiple uncertainties in reservoir connectivity, viscous oil and flow assurance. This paper demonstrates the development of digital fluid sampling techniques for deep-water fields using the RFG workflow to predict fluid properties and distribution, to address compartmentalization uncertainties and flow assurance risks, as well as to redefine the well-logging program. Identifying key reservoir concerns is the first step during the implementation of the RFG workflow. Five questions define key reservoir concerns: Do optical density measurements explain the impact of biogenic methane on fluid behavior? Is it feasible to characterize baffling and fault compartmentalization? Can we predict reservoir fluid properties and assess flow assurance risks based on fluid behavior? Is it possible to identify all this in real time? How could we optimize future fluid sampling programs? The next step is to collect the available DFA data and to integrate it with the existing PVT and geochemistry datasets. This paper describes the evaluation of over 150 fluid sampling DFA measurements acquired during the operational history of a Gulf of Mexico field. Fluid behavior and optical density gradients are interpreted from a geological perspective to understand reservoir connectivity. A strong correlation between optical density and asphaltene content enables digital fluid sampling for different PVT and geochemical parameters. Lastly, a general correlation of optical density and asphaltene content is derived for multiple Gulf of Mexico oil fields. Optical density measurements support a consistent characterization of biogenic methane along the studied deep-water field, suggesting a relation to fluid migration and charging from deeper to shallower reservoirs. Likewise, optical density gradients and its integrated evaluation facilitate the identification of mass transport complex (MTC) baffles in the north part of the field and the characterization of fault compartments in the main reservoir sands. In addition, the RFG workflow reveals the difference in fluid behavior of sampled wells located in the area of a water injection project by identifying asphaltene clustering near the oil-water contact. The correlations of optical density and asphaltene content help to predict fluid properties and to estimate its uncertainty, benefiting risk assessment for asphaltenes deposits and flow assurance in deep water operations. Real time analysis of optical density measurements during fluid sampling permits the characterization of fluid properties and reservoir connectivity, optimizing future fluid sampling programs when fluid contamination reaches 10%. Ultimately, this innovative methodology conveys a general correlation to predict asphaltene content based on optical density measurements for deep-water reservoirs in the Gulf of Mexico, enabling the possibility to predict reservoir fluid properties in real time fluid sampling operations.
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