Goliat is an ENI Norge-operated oil field located in the Arctic Barents Sea, 85 km NW of the city Hammerfest (Fig. 1). The Goliat reservoirs have a complex structural setting characterized by a large number of faults and a relative high structural dip towards the flank of the structure. This challenging combination calls for horizontal production wells for effective drainage. The Goliat field consists of several proven hydrocarbon reservoir units, but as of the date of this abstract, only Kobbe producers have been drilled. The Kobbe Formation is of Middle Triassic age and is divided into two main subgroups; the Upper Kobbe represents essentially a prograding deltaic system with mouth bars and tidal influenced lobes. In the Lower Kobbe, the system shifts into a more proximal, heterogeneous fluvial setting where sand bodies have limited lateral continuity. One particular challenge is that the well design requires the 8½-in. reservoir section to be initiated in the overlaying Snadd shale. To minimize shale exposure in the landing section aggressive build-up rates are employed, decreasing the length needed in shale. However, a steep approach may lead to deeper penetration in upper Kobbe sandstone, which can result in unwanted intra-shale drilling. Therefore, the key to successful well placement is the early detection of the reservoir top and the accurate mapping of the reservoir sand architecture remote to the wellbore. One way to successfully navigate a complex reservoir like Goliat is to use extra-deep azimuthal resistivity (EDAR) which can detect stratigraphic boundaries up 30m away from the wellbore in optimal resistivity environments (Hartmann, 2014). The development of advanced multi-component inversion modelling techniques (Sviridov, 2014) enhances the interpretations of resistivity data and can accurately provide real-time information regarding reservoir geometry. On Goliat, the EDAR service provided the capability to detect the top of the reservoir at about 20 m true vertical depth (TVD) and nearly 100 m MD before entering the reservoir, enhancing accurate wellbore landing. Extra-deep measurements also helped reduced the uncertainty in fault detection, where related throw can be estimated based on the displacement of boundaries. The use of a measurement with increased depth of detection (DOD), combined with advanced multi-component inversion while drilling techniques and real-time 3D visualization of data and reservoir model were vital to ensure the successful placement of the well. Real-time mapping of the reservoir geometry was key to optimize reservoir exposure.

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We present a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values.A commercial simulator provided by a tool vendor is utilized to generate a training dataset.The dataset size is limited because the simulator provided by the vendor is optimized for sequential execution.Therefore,we design a training dataset that embracesthe geological rules and geosteering specifics supported by the forward model. We use this dataset to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code.Despite employing a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multi-layer synthetic case and a section of a published historical operation from the Goliat Field.The observed average evaluation time of 0.15 milliseconds per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte-Carlo inversion algorithms within geosteering workflows.

The opportunity to detect boundaries ahead of the bit has long been a desire within the oil and gas industry as it would allow precise geo-stopping prior to entering unwanted formations and/or fluids. There are solutions already available such as the use of seismic applications. However these are for use on a different resolution scale, the accuracy needed for geo-stopping within meters of a formation boundary is lacking. Extra Deep Azimuthal Resistivity (EDAR) has been widely used for geosteering and landing applications around the world utilizing the common methodology of looking around and predicting ahead (see, for example, Larsen et. al., 2016). This methodology has been very efficient in a horizontal drilling environment with fairly low dipping layers, typically when incident angles do not exceed 10-15 degrees. In such wells, the environment ahead of the bit is generally not changing abruptly and therefore provides only a minor contribution to tool response compared to the volume around the tool. For higher incident angles, the zone of interest is likely to be ahead of the bit. Here traditional looking around the wellbore and predicting ahead is no longer sufficient. The steep angle of approaching beds requires sensitivity ahead of the bit in order to detect and predict upcoming resistivity boundaries. In this paper we will study the extended capabilities of the interpretation method currently used only in high angle and horizontal (HA/HZ) well applications. We will present sensitivity analysis of the theoretical capability of detecting ahead of bit with the different key factors determining the capability, such as sensor placement behind bit, formation resistivity and incident angle. The study will show that the application range of EDAR can be extended from the 15° incident angle range to up to 90° incident angle. The extension allows using the service for low angle geo-stopping applications. The analysis is performed on theoretical data as well as a case study from a well in the Middle East.

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