Horizontal well type was selected to optimize drainage of a mature field which had less than 3 years prior concession end. However, the well construction of horizontal well is relatively more challenging compare to other well type which will lead to higher cost. Offset well execution data showing that the challenges of drilling this well type were wellbore instability, drill bit worn out due to gravel zone, uncertainty on marker prognosis and the potential differential sticking on lateral section (inside the depleted reservoir) especially during running pre-perforated liner as completion equipment. It was realized that drilling performance improvement is required to reduce the cost and accelerate the production. To overcome the challenges, several technologies were introduced. Drilling fluid optimally designed to support borehole stability and real-time adjustment were performed by analyzing the drill cutting. Ridged shape cutter bit utilized to aid during penetrating the gravel zone. To maximize the penetration rate, point-the-bit Rotary Steerable System was utilized for better weight transfer and improved wellbore geometry compare to conventional mud motor system. To manage uncertainty of marker prognosis, the Bottom Hole Assembly (BHA) was designed to achieve maximum dog leg severity capability by minimizing the number of stabilizers. Near bit gamma ray sensor also installed to aid timely formation marker identification. To mitigate risk of differential sticking, the bridging agent sized to fit with the formation permeability through particle size distribution analysis. Centralizers also installed on pre-perforated liner assembly to minimize contact area which might cause differential sticking. Though these technologies application has been used on many occasions, they were new to this field development which raised concern over their potential risk, especially with limited investment period. Therefore, a series of execution assurance processes were performed. Cross-functional risk assessment sessions were held to review trade-offs of each new technology potential value with the potential cost incurred by considering the possibility of success and potential changes on other aspect of the well construction that need to be adjusted to accommodate the application and mitigate its potential risk. Then a drill-well-on-paper session held with all personnel that would involve by simulating the drilling execution following the procedure which then improved by creating mitigation steps or provide clarity. Artificial intelligence assisted decision support center was also utilized to monitor execution and aid performance improvement recommendation utilizing geomechanics, hydraulic, torque and drag model as well as the field best practices. These all result to 17% cycle time improvement with 9% lower cost compare to typical well. The production sweet-spot zone exposure also increased by 20%.
For years, the oil and gas industry has been utilizing real-time sensors to monitor multiple parameters. In drilling operations, these sensors are highly important in achieving the well objectives. They provide data to guide the well execution and aid during the evaluation phase. The downside is the limited capability to identify operational anomalies. The latest popular strategy to mitigate this downside is using a Decision Support Center to obtain the most benefit from this data. Decision Support Center should be available for 24-hour operations and consists of multiple personnel to review data, provide path forward recommendation, and alert the field supervisor. During these past years, most companies have optimized the number personnel to keep competitive in a low oil price environment. However, some knowledge has been lost during the process. On the other hand, the industry worldwide is entering an era of digital transformation. This triggers the need for artificial intelligence systems that can translate analog knowledge from personnel to digital data, analyze for execution anomalies from sensor readings, and provide feedback and recommendations to the end-users. In one of largest mature onshore Indonesian blocks, a pilot implementation of artificial intelligence assisted Decision Support Center was developed. With limited time and cost, the project needed to be developed as fit-for-purpose. Thus, the system focused on the significant problem which historically caused million dollars in lost opportunity in the form of stuck pipe. A single stuck pipe event could result in a US$ 2.5 MM loss, which is more than 2 times the typical well AFE in this block. Beside surface and downhole sensors readings, subsurface prognosis, torque and drag model; operational best practices were also included in the system. Anomaly alarms were developed, including ECD limit by depth, pick up weight limit by depth, torque limit by depth, potential loss circulation zone, maximum allowable static time, and maximum allowable pumps off time. Whenever the sensor reading exceeds these limits, an automatic alarm would be generated and sent to the field supervisor, drilling superintendent and drilling engineer. As result, it only required 1 personnel per 4 rigs to maintain the process compare to 3-4 personnel per rig required with the conventional Decision Support Center process. This process successfully eliminated stuck pipe events though to the remaining of the drilling campaign of 56 wells, where the historical frequency had been 1 stuck pipe event out of 20 wells. Future improvement in this system will utilize machine learning to develop anomaly alarms by gathering data from offset well operations. An additional benefit of this system is to implement it in workover operations.
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