The Formation Pressure While Drilling (FPWD) has been widely used in the industry in different downhole environments for formation evaluation purposes – pressure gradients, fluid identification and contacts, and reservoir pressure management. However, there is a niche application of this tool that no wireline formation tester could provide – real-time drilling optimization. Drilling reservoirs with abnormal high pressure is a complex process. The overpressure can cause a well to blowout or become uncontrollable during drilling leading to catastrophic outcome if too low mud weight is selected. On the other hand, too high mud weight can lead to lost circulation, decrease in rate of penetration, stuck pipe and most importantly it can damage the formation hindering productivity of the well. The monitoring of actual pore pressures in real-time with FPWD tool is critical to make proper mud weight adjustments, rather than relying on inferred pore pressure from either predictive models or offset wells. The paper discusses a case study of application of FPWD in a well drilled in a fault block never drilled before, where pore pressure data from wells located in nearby blocks indicated that there may be abnormal high-pressure zones. Pressure measurements were performed while drilling after every 100 m upon reaching a certain depth. Since downhole BHA included gamma ray and resistivity tools along with FPWD tool, the combination of this data was used to select the intervals to be tested. The study shows how real-time pore pressure measurements helped to adjust mud weight from 1.45 to 1.80 sg to avoid the risk of formation fluid influx into the well, optimize drilling operations and, based on the acquired results, an operator decided on the total depth (TD) of the well. Since pretests were performed at targeted layers, formation pressure and mobility data also aided in reservoir characterization. The paper presents the first successful application of FPWD technology in Russia and Caspian region for drilling optimization purposes in overpressured reservoirs. Real-time data availability allowed making quick operational decisions to safely drill the well until planned TD.

Brownfield field development plans (FDP) must be revisited on a regular basis to ensure the generation of production enhancement opportunities and to unlock challenging untapped reserves. However, for decades, the conventional workflows have remained largely unchanged, inefficient, and time-consuming. The aim of this paper is to demonstrate that combination of the cutting-edge cloud computing technology along with artificial intelligence (AI) and machine learning (ML) solutions enable an optimization plan to be delivered in weeks rather than months with higher confidence. During this FDP optimization process, every stage necessitates the use of smart components (AI & ML techniques) starting from reservoir/production data analytics to history match and forecast. A combined cloud computing and AI solutions are introduced. First, several static and dynamic uncertainty parameters are identified, which are inherited from static modelling and the history match. Second, the elastic cloud computing technology is harnessed to perform hundreds to thousands of history match scenarios with the uncertainty parameters in a much shorter period. Then AI techniques are applied to extract the dominant key features and determine the most likely values. During the FDP optimization process, the data liberation paved the way for intelligent well placement which identifies the "sweet spots" using a probabilistic approach, facilitating the identification and quantification of by-passed oil. The use of AI-assisted analytics revealed how the gas-oil ratio behavior of various wells drilled at various locations in the field changed over time. It also explained why this behavior was observed in one region of the reservoir when another nearby reservoir was not suffering from the same phenomenon. The cloud computing technology allowed to screen hundreds of uncertainty cases using high-resolution reservoir simulator within an hour. The results of the screening runs were fed into an AI optimizer, which produced the best possible combination of uncertainty parameters, resulting in an ensemble of history-matched cases with the lowest mismatch objective functions. We used an intuitive history matching analysis solution that can visualize mismatch quality of all wells of various parameters in an automated manner to determine the history matching quality of an ensemble of cases. Finally, the cloud ecosystem's data liberation capability enabled the implementation of an intelligent algorithm for the identification of new infill wells. The approach serves as a benchmark for optimizing FDP of any reservoir by orders of magnitude faster compared to conventional workflows. The methodology is unique in that it uses cloud computing technology and cutting-edge AI methods to create an integrated intelligent framework for FDP that generates rapid insights and reliable results, accelerates decision making, and speeds up the entire process by orders of magnitude.

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