The recent development drilling campaign at Mubadala Petroleum's offshore Nong Yao field faced many challenges, one of which is the complexity of the reservoir which consists of mixed sand-shale sequencies with thin sand lobes of varying thicknesses. To tackle these challenges and to maximize recovery, Mubadala Petroleum planned four horizontal wells for this campaign. However, the conventional methods of geosteering have limitations. For instance, the distance-to-boundary mapping tool typically does not provide large enough depth-of-investigation for the operator to see through the interbedded shale layer to identify the multiple target sand lobes, which could pose limits on the production optimization and ultimately on the final recovery rate. Fortunately, a new technology emerged at the start of the campaign with a potential for a much larger depth of investigation and a better mapping resolution. This multilayer mapping-while-drilling tool was an extension of the previous tool with additional sensors that could read deeper into the formation. Coupled with a new advanced automatic inversion process which utilizes powerful Cloud computing, the subsurface formation resistivity profiles around the wellbore could be mapped clearly up to 25 ft away from the tool, while providing a multilayer mapping with up to 8-layer mapping capability. This new technology was evaluated and applied in two wells in this campaign to resolve the above-mentioned challenges. The result was a resounding success for the Mubadala led drilling team. In this paper, the authors explain the technology, the process of evaluating and applying it to operation, and the results from applying it. This was the first time that this technology was used in Thailand and this case study summarizes a successful outcome. The mapping results from the tool will also be used to update the reservoir model during the post-job phase and provide improvements of the overall reservoir characterization of the field.

In hydrocarbon exploration, rock physics analysis plays a key role by connecting seismic data to rock properties. Analysis of rock physics data enables geophysicists to understand how fluid content affects the seismic response and what they should look for to improve the chance of finding hydrocarbons. In the Nong Yao oil field, the use of rock physics and AVO analysis was used to improve the hydrocarbon prediction process. The preferred method starts with rock physics analysis of key wells. Fluid Replacement Modelling (FRM) is then performed across many wells in order to generate a predicted seismic response for different pore fluids (gas, oil and brine). The predicted AVO response is then calibrated against the actual AVO response from the seismic data from key wells in order to build a database. In the Nong Yao Field, over four hundred and fifty drilled data points from sixty-nine wells were utilized in the analysis. This database is analyzed in order to find the optimal combination of parameters for hydrocarbon prediction, which is then used to improve hydrocarbon prediction for future near-field drilling candidates. Near-field appraisal programs in the Nong Yao oil field are driven strongly by amplitudes and AVO, as rock physics analysis has shown that sands and shale lithologies can be easily discriminated based on acoustic impedance. Fluid prediction is more difficult based on acoustic impedance alone, as other factors such as variable sand thickness and seismic data quality mean that there is significant overlap between hydrocarbon and wet sands. Rock physics analysis has shown that AVO behavior can be included to provide better separation between hydrocarbon sands and wet sands. AVO signatures from all the data points are then analyzed using intercept vs gradient cross-plots. A background wet trend is defined with the clear observation that increasing distance from the background wet trend correlates to increasing chance of hydrocarbon fill. Data are categorized into weak, moderate and strong AVO response based upon their distance from the background wet trend and then this is used to modify the chance of success of near-field appraisal drilling targets utilizing conditional probability. This results in an increased chance of success of up to 20% in a strong AVO supported target and around 10% in a moderate AVO supported target. Targets are then quantitatively high-graded in an appraisal portfolio.