Geosteering within thin clastic reservoirs can be extremely difficult due to their varying thicknesses, lateral facies changes and changing channel geometry. The efficiency of traditional triple-combo tools does not help achieve this objective due to their lack of azimuthal sensitivity. This paper presents a case history of reservoir navigation within a clastic reservoir in Saudi Arabia utilizing deep azimuthal resistivity, reservoir navigation modeling, and multi-component while drilling (MCWD) resistivity inversion.A number of challenges needed to be overcome for this well to become a successful oil producer. Firstly, the development and lateral continuity of the reservoir sand was not guaranteed. Reservoir sand developed at the bottom of the formation in all nearby offset wells. The development in the upper part of the target formation however, varied throughout the field. In addition, further uncertainty was present regarding position of the oil-water contact (OWC).A reservoir navigation strategy was devised utilizing a full triple combination of logging while drilling (LWD) tools that included deep azimuthal resistivity and azimuthal density. In addition, MCWD resistivity inversion software was used to ascertain the resistivity of the shoulder beds through inversion of real-time resistivity and azimuthal resistivity curves. The reservoir navigation strategy comprised maintaining a well inclination of 85 degrees until a clean reservoir sand was penetrated. MCWD would provide the first indication of the approaching reservoir, and well inclination would be increased in anticipation of reservoir entry. Upon entry in the reservoir, the deep azimuthal resistivity data, used in conjunction with reservoir navigation, would keep the well in the reservoir until TD or until the sand pinched out. In addition, MCWD would be used to ascertain the lower conductive boundary resistivity, either shale or water.This strategy was successfully implemented and resulted in 1,400 ft of reservoir contact, achieving 100% Net: Gross. The deep azimuthal resistivity prevented reservoir exit in spite of higher-than-anticipated formation dip and a thinning reservoir towards the toe of the well. MCWD successfully proved that the lower conductive boundary was shale, not water, and confirmed the distance to the boundary results from the deep azimuthal resistivity tool.
Tar mats are encountered in many Middle East carbonate reservoirs including the Kingdom of Saudi Arabia. Tar is considered to be very heavy crude that does not flow or has very limited flow capacity at standard reservoir conditions. In some cases it is found above the aquifer and below a light oil layer. As tar is practically immobile at reservoir conditions it acts as a flow barrier between water and light oil, causing significant challenges for reservoir development. In such cases water injectors work most efficiently when placed horizontally just above the tar mat to maintain pressure support during production. One challenge when drilling for these water injectors is to optimize their position above the tar layer yet as close as possible to the tar to maximize sweep efficiency. A further complication for their placement is the uncertain lateral distribution of the tar in terms of true vertical depth (TVD). The top of the tar layer is often an undulating lateral surface with varying depths. Positioning the well a "safe" distance from the tar is not a good solution as it might leave behind a significant volume of producible oil, ultimately lowering oil recovery. A real-time tar detection method is needed while drilling to quickly respond and modify the wells trajectory if tar is encountered. The low mobility caused by tar can be detected with a formation pressure while drilling (FPWD) tester. A nuclear magnetic resonance (NMR) tool provides oil viscosity estimation and additional tar-related properties. In combination with a conventional logging string, tar can be positively detected while drilling. This case study from a carbonate field in KSA shows the successful placement of a 6.25-in. horizontal well using a logging-while-drilling (LWD) NMR-device and an LWD formation pressure tester to detect tar in combination with conventional resistivity, density and neutron (triple-combo) LWD logs.
Understanding of rock strength, and its variability along the length of the well, is essential for building an efficient well trajectory during geosteering operations. Traditionally, drill cuttings, surface gas analysis, measurements while drilling (MWD) data and Logging While Drilling (LWD) measurements have been used to optimize trajectories. Rock mechanical properties, derived from petrophysical well logs are key to drilling, production and recovery potential of the well: However, in a vast majority of geosteered wells, LWD data and the derived rock properties are not available thus conforming to the given well trajectory and successful Geosteering is difficult. In comparison, real-time downhole drilling data is usually available but rarely used. An innovative, reliable and robust method is presented which capitalizes on downhole MWD and LWD data. This method uses downhole weight-on-bit (WOB), rotational speed (RPM), downhole torque (TOR), and rate-of-penetration (ROP), to characterize the mechanical specific energy (MSE) consumed in the drilling process. The specific bit diameter (D), mud-weight (MW) and depth (TVD) of drilling are also used in the model. If the task is to optimize drilling parameters for a new formation (e.g. drill-off-test), then "minimum" MSE is captured. However, if the task is continuous drilling, geosteering, and creating a stable well for its subsequent stage and cluster-wise hydraulic fracture design, then "instantaneous" MSE is used to infer strength of the rocks and their variation along the length of the well. An offshore well from the North Sea was initially selected to apply the concept of the above technology on several post well data analyses using downhole drilling data together with average ROP and RPM. Further, the same concept was used in a real-time application with downhole drilling data. The gamma-ray, neutron porosity, density and resistivity were analyzed and compared with the MSE obtained. Drilling efficiency was assumed based on prescribed industry standards for calculating confined compressive strength (CCS), Internal Friction Angle (IFA), and unconfined compressive strength (UCS). The UCS estimated at a scale of 1.0-1.5″ scale versus depth-of-cut (scale of 0.1-0.5″) resolution matched well with log based UCS from density, porosity and acoustic logs. Calculated results are compared with lab-based core test data where available. The details of these calculations and successful application to Geosteering are presented. These strength estimates are of benefit to directional drilling engineers for safe and economic well placement along optimum well trajectory, better well production and economic recovery from successive multi-stage and stage-and-cluster hydraulic fracturing designs. An ‘Efficiency’ Factor’ used in the process is discussed which originates from strengthening of rocks due to friction, chip-hold-down effect on cuttings, strengthening due to dilatancy, and cuttings-extrusion like behavior prevalent in drilling.
A horizontal producer was planned in Ahmadi limestone crossing two major faults in the curve section of the wellbore. A possibility of small sub-seismic faults associated with these two major faults was a real possibility. The throw of these subseismic faults can be substantial and well placement can become challenging. A geosteering strategy was created to mitigate structural uncertainty and maximize reservoir exposure. This was achieved by constructing a detailed earth model that utilized deep azimuthal resistivity and high-resolution electrical images.The use of high-resolution electrical images helped in identifying the faults in real time. This information in conjunction with deep azimuthal resistivity signal strength and the distance to bed (D2B) calculation and conventional logging-while-drilling (LWD) formation evaluation data was incorporated in reservoir navigation software to calculate the fault throw. Based on this information, a 2D reservoir navigation model was updated regularly to adjust the wellbore trajectory for optimum well placement.This paper discusses how above integrated solution was invaluable in placing the wellbore optimally in the zone of interest by using deep azimuthal resistivity signal strength and D2B calculation in conjunction with high-resolution electrical resistivity measurement. In the subjected well, a number of small faults were determined in real time with throws ranging from 9 to 16ft. Based on this information, decisions were made to readjust the well trajectory and the wellbore was placed optimally in 2071ft MD lateral section to achieve 96% net to gross.
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