Best Practices and Recommendations for Magnetic Directional Surveying in the Barents Sea

摘要

In the coming years, several oil and gas fields in the Barents Sea will be developed. The Barents Sea is located between 71-74° latitude and is a part of the Arctic Ocean. At such high latitudes, surveying with magnetic Measurement-while-drilling (MWD) and gyro survey instruments result in almost twice the azimuth uncertainties of the North Sea latitudes. Magnetic MWD is the most common survey method, mainly due to low cost and time consumption to perform a measurement. However, the main drawback with magnetic MWD survey instruments is the sensitivity to external magnetic fields resulting from magnetic components in the bottom hole assembly (BHA), magnetic particles in drilling fluids, and fluctuations in the Earth's magnetic field. The Barents Sea is in the auroral zone where the largest geomagnetic field disturbances are experienced. Consequently, achieving accurate reference field parameters for MWD surveying is challenging. Different methods exist to maintain an acceptable quality of magnetic survey data at high latitudes. One approach is to isolate the magnetometer sensors from external magnetic interference and apply uncorrected azimuth calculation. This is advantageous because uncorrected azimuth is less sensitive to external field disturbances than corrected azimuth achieved by drill string interference (DSI) correction or multi-station analysis (MSA). For offshore sites, limitations exist for using geomagnetic reference data from land-based monitoring stations to correct MWD data. Large azimuth uncertainties result in major lateral wellbore position uncertainties and give less room for blunders and gross survey errors when high accuracy is required. Consequently, it is vital to minimize the effects of external error sources. Several downsides of large position uncertainties exist, such as increased distance between wells to avoid collisions and decreased chance of hitting geological targets. The combined effect of external error sources is complex and in specific cases certain error sources cannot be detected using conventional quality control procedures. In this paper, recommendations to overcome the above-mentioned challenges will be presented.