The precise landing and steering of horizontal wells using conventional mudlogging and Logging While Drilling (LWD) data is a particular challenge for the Lebăda Field, offshore Romania. The use of a new technique of elemental geochemistry analysis (or chemosteering) became an option for the identification of Cenomanian, Turonian–Coniacian–Santonian, Campanian and Eocene strata. This has enabled more accurate placement of the horizontal development wells within the desired reservoir target interval. Geochemical data enabled the identification of chemostratigraphic zones C1, C2, C3 and zone R that correspond to the reservoir section. The application is a result of the geochemical zonation performed using elements and ratios that are sensitive to depositional environment, sea level change, heavy mineral concentrations and siliciclastic input namely: Sr/Ca, Zr/Th, Si/Zr and Si/K. In ascending stratigraphic order, the ratio thresholds of zone C3 are Zr/Th > 11, Sr/Ca > 1.1, Si/Zr < 22 and Si/K < 19, while zone R corresponds to 5.5 < Zr/Th < 11, Sr/Ca < 1.1, Si/Zr > 22 and Si/K > 19. C2 zone is defined by Zr/Th < 5.5, Sr/Ca > 1.1, Si/Zr < 22 and Si/K < 19 and C1 zone is characterized by Si/Zr > 22 and Si/K > 19. The selected geochemical ratios indicate a strong geochemical zonation. In the case of offset wells, 85.9% of the data confirmed the proposed classification and 89.4% for the real-time application case. The zone R shows a strong contrast with the surrounding formations facilitating critical decisions during well placement and geosteering, increasing the reservoir exposure by 28%. The quantitative approach delivered very valuable results, providing a solid foundation to define correlation and well landing intervals. Simultaneously, the cost of the method represents a fraction of the LWD cost and 0.15% of the total project cost, making it very cost effective and a standard approach for future projects.

The water saturation (Sw) interpretation from resistivity logs has been observed to be highly uncertain in Oilfield A. By integrating core fluorescence observations from underutilized ultraviolet (UV) light core photography images, it is possible to QC these interpretations in challenging thin reservoirs, thinly bedded reservoirs, compacted reservoirs and low relief structures with significant oil volumes at intermediate oil saturation. High-quality core is routinely taken in appraisal wells in Oilfield A. A standard core analysis work programme has been applied to these cores including core gamma, Conventional Core Analysis (CCA), Whole Core Analysis (WCA), Mercury Injection Capillary Pressure (MICP) and core photography. Core photography includes both white light and UV imaging. A detailed core description of the cores has subsequently been made. This includes the description of core fluorescence in UV core photography images which show varying intensities of hydrocarbon saturation. The core fluorescence description is integrated with all other available well data including the log Sw interpretation. The results highlight the uncertainty in the conventional resistivity-based determination of Sw. As part of the modeling workflow in Oilfield A, log Sw interpretation and the proposed saturation height model (SHM) were QC’ed by integrating all available data from appraisal wells. By comparing hydrocarbon core fluorescence from UV light core photography images to the log Sw interpretation, the following results were possible: High resistivity "shoulder bed" effects, resulting from the low-resolution resistivity log averaging the juxtaposition of low porosity non-reservoir and reservoir intervals, were removed from petrophysical interpretation. Core fluorescence from UV light core images was observed to be intermittent on a foot-to-decifoot scale in Reservoir 5 due to cementation associated with chemical compaction features. The high-resolution core fluorescence description was used to challenge the continuous hydrocarbon saturation interpreted from logs in Reservoir 5. The integration of these observations allowed reconciliation with results from repeat formation tester pump-outs and well tests, enabling a more realistic Sw distribution in the reservoir model. Hydrocarbon saturation from log Sw was interpreted to continue deep into several reservoirs in Oilfield A. The core fluorescence description from UV light core photography showed hydrocarbons were only present at the top of these reservoirs. In reservoirs with homogeneous porosity profiles but vertically changing rock-types, the resistivity log is not able to distinguish high resistivity caused by hydrocarbon saturation from high resistivity caused by cementation and/or reduced permeability. The finding challenges using the same values of ‘m’ and ‘n’ in Archie log Sw interpretation throughout a reservoir. Integration of hydrocarbon core fluorescence from UV light core images added significant value to the understanding of Sw distribution in Oilfield A. Apparently contradictory data from repeat formation tester pump-outs and well tests were reconciled, and previous hypotheses were challenged by integrating this underutilized data type. Simple steps are used to integrate these data to reduce Sw uncertainty in heterogeneous carbonate reservoirs, particularly those with a large proportion of STOOIP at intermediate oil saturation due to their low-relief structure.

Reservoir 2 in Oilfield A shows strong evidence of variable chemical compaction. The south of Reservoir 2 is up to 20% thinner than the north with 50% lower average porosity. Stylolites are more abundant in the south than the north. Fractures are observed in multiple data types associated with stylolites. A stratigraphically-constrained, fractured reservoir concept is essential to understand the higher-than-predicted water cut of production wells on the southern flank of the structure. High quality core is routinely taken in appraisal wells in Oilfield A. A detailed core description was undertaken including recording the precise depth and amplitude of chemical compaction features including stylolites, their associated fractures and their diagenetic cement fill. Core based observations were calibrated to wireline wellbore images (WBI) and from there to logging while drilling (LWD) WBI in horizontal development wells. These data were integrated with information from production logging tool (PLT) runs. As a result it was possible to build a fractured reservoir concept, vertically and laterally constrained by static data and conditioned by dynamic data. In the south of Reservoir 2, Oilfield A, open or partially open Mode 1 fractures are often observed from core observation propagating 5-15cm above and below abundant stylolites. The more compacted, thinner reservoir in the south is also more cemented, more brittle and therefore more susceptible to fracturing than the north. As such, core provides a 1D view of the reservoir. The key uncertainty in developing the fracture concept, is to understand the lateral extent and connectivity of such features. WBI interpretation of stylolite-related fracturing was essential to understand their abundance and orientation in 3D. The connectivity of these features is inferred when combined with PLT and well production data. Core-scale observation, combined with the WBI fracture dataset, was upscaled to the 3D seismic dataset. Acoustic impedance from 3D seismic shows a strong negative correlation with reservoir thickness and porosity. Since stylolite-related fractures are most abundant in the thinnest, lowest porosity part of the reservoir, fractures could be vertically distributed within the reservoir by WBI and laterally distributed by seismic (acoustic impedance) response. Integration of this concept in the dynamic model resulted in a better history match of water cut behaviour in production wells on the southern flank of the structure. Traditionally the role of stylolites in oil reservoirs has focused on their impact reducing permeability and baffling transmissibility, not on increasing them. All oil reservoirs are fractured to a greater or lesser extent and traditionally more focus has been placed on tectonic fractures. Highlighting the role that short, bed bound, stylolite-related fractures play in enhancing permeability is essential in understanding their impact on fluid movement within carbonate reservoirs.

Static grids are commonly an ensemble of millions of reservoir property values. Analyzing this vast dataset is a challenging task and is usually performed via simple statistical parameters (e.g arithmetic mean and standard deviation). The objective of this paper is to present a quick and efficient way of analyzing static model realization results in a more visual, interactive and efficient manner allowing a multidisciplinary team to absorb information and key ideas quickly. The proposed methodology starts with a set of properties from a given static grid realization. The data are prepared with all necessary labeling and categorization to be analyzed (e.g. reservoir and segment indicators, well regions etc.). All kinds of data variations available in the static model are gathered and mobilized into dynamic data dashboards for further analysis. The interactive data visualization templates provide flexibility to filter information either by categories or data ranges. The filtering schemes are immediately propagated to all available plots in the dashboard highlighting common patterns from all the data relationships in the static model. The principle output from this methodology is an interactive data visualization panel that displays all necessary infographics related to reservoir data-trends. Starting from simple rock quality pie-charts, which can be mapped showing field-wide trends, porosity-permeability plots, to more detailed infographics of property ranges per well/reservoir indicator as well as summary tables of average properties per reservoirs/layers/segments etc. The ability to have all plots linked in a single display provides a simple but powerful platform to evaluate and interrogate data from the static grid using different views. Data can be aggregated in different ways and summarized accordingly. With this continuous process of data filtering, it is possible to quickly identify outliers and collect evidence of data inconsistency within the static grid. The additional value of the presented approach is the ability to compare multiple property realizations in a single template and summarize similarities and differences between different static realizations. The implementation of reservoir property dashboards proved to be an effective practice to increase the understanding of distributed properties inside static grids. A more comprehensive and detailed data quality check can be performed in multiple properties using a single view of the structured dataset. Additionally once data is loaded into the data visualization platform it can be easily shared with team members without the need of any specialized modeling software.

& Introduction Agile Project Management has received a significant amount of attention in the oil and gas industry over the past five years. Companies have increasingly focused on digitalisation in an attempt to generate additional value from their existing business, and to keep up in a fast changing global marketplace. The uptake of an Agile Approach in the industry has however, been markedly slower than in other industries. Management consultants promoting an agile approach have often presented case studies with limited relevance to oil and gas projects, which has led to scepticism within the workforce. An Abu Dhabi offshore company successfully applied techniques adapted from Agile Project Management during a major reservoir modelling project of a giant oil field. Elements of the agile approach, such as the concept of the Minimum Viable Product, were found to be key enablers in the development of more efficient reservoir modelling workflows. Furthermore, these techniques were effective in overcoming challenges related to remote working, resource constraints, and contradictory field data. This paper introduces the Agile Project Management approach and shares experience of how it can be applied effectively to subsurface modelling projects.