Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from nonbeing field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (T), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination (R 2 ) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination (R 2 ) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of −0.67, and an absolute average relative error of 3.18.
Precise prediction of pore pressure and fracture pressure is a crucial aspect of petroleum engineering. The awareness of both fracture pressure and pore pressure is essential to control the well. It helps in the elimination of the problems related to drilling, waterflooding project, and hydraulic fracturing job such as fluid loss, kick, differential sticking, and blowout. Avoiding these problems enhances the performance and reduces the cost of operation. Several researchers proposed many models for predicting pore and fracture pressures using well log information, rock strength properties, or drilling data. However, some of these models are limited to one type of lithology such as clean and compacted shale formation, applicable only for the pressure generated by under compaction, and some of them cannot be used in unloading formations. Recently, artificial intelligence techniques showed a great performance in petroleum engineering applications. Hence, in this paper, two artificial neural network models are developed to estimate both pore pressure and fracture pressure through the use of 2820 data sets obtained from drilling data in mixed lithologies of sandstone, carbonate, and shale. The proposed artificial neural network (ANN) models achieved accurate estimation of pore and fracture pressures, where the coefficients of determination ( R 2 ) for pore and fracture pressures are 0.974 and 0.998, respectively. Another data set from the Middle East was used to validate the developed models. The models estimated the pore and fracture pressures with high R 2 values of 0.90 and 0.99, respectively. This work demonstrates the validity and reliability of the developed models to calculate pore and fracture pressures from real-time surface drilling parameters by considering the formation type to overcome the limitation of previous models.
Although devised in 2003, managed pressure drilling (MPD) has gained widespread popularity in recent years to precisely control the annular pressure profile throughout the wellbore. Due to the relatively high cost and complexity of implementing MPD, some operators still face a challenge deciding whether or not to MPD the well. In the offshore Mediterranean of Egypt, severe to catastrophic mud losses are encountered while conventionally drilling deepwater wells through cavernous fractured carbonate gas reservoirs with a narrow pore pressure-fracture gradient (PP-FG) window, leading to the risk of not reaching the planned target depth (TD). Furthermore, treating such losses was associated with long non-productive time (NPT), massive volume consumption of cement, and lost-circulation materials (LCM), in addition to well control situations encountered several times due to loss of hydrostatic head during severe losses. Accordingly, the operator decided to abandon the conventional drilling method and implement MPD technology to drill these problematic formations. In this paper, the application of MPD is to be examined versus the conventional drilling in terms of well control events, NPT, rate of penetration (ROP), mud losses per drilled meter, LCM volume pumped, and drilling operations optimization. According to the comparative study, MPD application showed a drastic improvement in all drilling performance aspects over the conventional drilling where the mud losses per drilled meter reduced from 19.6 m3/m to 3.7m3/m (123.2 bbl/m to 23.4 bbl/m). In addition to that, a 35% reduction of NPT and also a 35% reduction of LCM pumped, and 67.2 % reduction by volume of cement pumped to cure the mud losses. Moreover, the average mechanical rate of penetration increased by 37.4%. MPD was also credited with eliminating the need for an additional contingent 7" liner which was conventionally used to isolate the thief zone. The MPD ability to precisely control bottom hole pressure during drilling with the integration of MPD early kick detection system enables the rapid response in case of mud loss or kick, eliminating kick-loss cycles, well control events, and drilling flat time to change mud density. This paper provides an advanced and in-depth study for deep-water drilling problems of a natural gas field in the East Mediterranean and presents a comprehensive analysis of the MPD application with a drilling performance assessment (average ROP, mud losses, LCM and cement volumes, well control events) emphasizing how MPD can offer a practical solution for future drilling of challenging deepwater gas wells.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.