No abstract

No abstract

Based on the successful utilization of advanced mud gas (AMG) for fluid identification in five production/injection wells in the Snorre field, the fluid identification while drilling technology was deployed for a seismic anomaly within the overburden at the Kyrre formation level. The main objective was to identify the reservoir fluid type (oil versus gas) within the anomaly and to use this information to potentially de-risk a similar shallower seismic anomaly - the Linga prospect at the top Shetland level. Fluid identification while drilling is an award-winning innovation that has been broadly used in exploration and production wells at Equinor. The digital technology combines mud gas and PVT data for accurate reservoir fluid typing and property predictions. Snorre field is one of the first users of the technology and accumulated good experiences regarding the capacity and limitations in reservoir zones. Due to the lack of other good tools to identify the hydrocarbon in overburden in a cost-efficient manner, the Snorre field decided to deploy the fluid identification technology for the task. Utilizing AMG data in the overburden for the four Snorre Expansion Project (SEP) wells showed satisfactory results. Reservoir oil was identified with confidence in the Kyrre formation for the first three wells, and no additional logging was necessary. The 4th well was drilled with higher ROP (above 30 m/hr) and proved a similar oil signature without compromising the data quality. The main objective was met, the fluid type in the Kyrre anomaly was confirmed, and this result was a de-risked Linga prospect. The probability of producing the Linga prospect has increased due to the accurate reservoir fluid type. The experiences in the overburden from the Snorre field show fluid identification from mud gas is a cost-efficient tool and has the potential to be utilized broadly in the overburden. With an accurate fluid identification in the overburden, we can achieve safety assurance, reduced drilling costs, and matured production prospects.

Advanced mud gas logging has been used in the oil industry for about 25 years. However, it has been challenging to predict reservoir fluid properties quantitatively (e.g., gas oil ratio – GOR) from only the advanced mud gas data (AMG) while drilling. Yang et al. proposed the first accurate GOR predictive model in 2019 from advanced surface data based on a machine learning algorithm. Since then, the method has been applied to both conventional and unconventional fields with good results. For our Norwegian operational units, we are developing a real-time service for fluid identification to optimize fluid sampling in exploration wells and support production drilling. Here, quantitative information about reservoir fluids will support the teams to take wellinformed decisions with respect to well placement, petrophysical log interpretation, and optimizing production by improving the selection of perforation intervals. We utilize a standard wellbore software platform to integrate the following data for fluid identification: AMG data, various AMG QC tools, normalized total gas response, GOR prediction, and petrophysical logs from logging while drilling (LWD). The proposed work approach integrates the information from multiple disciplines and makes the real-time fluid identification task much more reliable for operational decisions. We selected two field cases to demonstrate the approach of integrating AMG data and petrophysical logs. The first field case is an exploration well with multiple reservoir zones planned as production targets. The integrated approach shows reservoir fluids from all reservoir zones are almost identical. Consequently, we reduced the sampling program and only sampled at the best reservoir zone for cost efficiency. The second field case is a mature field being produced by pressure support from water, gas or water alternating gas injections. When a new production well is drilled, there is always a question of whether it encounters any injection gas. We applied the new approach to several production wells and obtained satisfying result. The latest information from the predictive GOR model solved many puzzles in petrophysical interpretations. This paper presents a new approach for reservoir fluid identification by integrating advanced mud gas data and petrophysical logs while drilling. This new approach makes real-time operational adjustments possible based on reservoir fluid identification along the well. The business potential is significant for accurately mapping resources for in-fill wells, boosting profitability, and lowering carbon footprint.