Sustained casing pressure, otherwise known as casing-casing annulus (CCA) pressure, is a common problem encountered in the deep, high pressure, high temperature (HPHT) gas fields of Saudi Arabia, and in many other locations globally. Although many solutions have been tried in these fields over the years, none of the existing solutions have proven to be 100% effective. A new solution has recently been implemented in Saudi Arabian gas fields that features a combination of heavy-weight cement blends greater than 21ppg and a polymer resin to improve the mechanical properties of the cement, especially the shear bond, to prevent the CCA pressure. Polymer resin is also resistant to hydrocarbons, acids, and salts, enabling the cement-resin system to be placed in harsh environments. This resistance will help to maintain a dependable barrier throughout the life of the well. This paper presents a case history of the application of this heavy-weight cement-resin (HWCR) system in the Saudi Arabia Harradh field where the failure of a differential valve (DV)packer meant that there would be no redundancy backup should the cement fail to provide a full barrier in the annulus. The paper describes the process used to design the HWCR system and how its application is critical to the success of the job.

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High pressure and high temperature (HPHT) wells especially those with narrow pore / fracture pressure gradient margins present challenges in drilling. Maintaining optimum and low rheology for such wells becomes a challenge where a slight change in the bottom-hole pressure conditions can lead to non-productive time. However maintaining low viscosity profile for a drilling fluid can pose a dual challenge in terms of maintaining effective hole-cleaning and barite-sag resistance. This paper describes the formulation of 95pcf medium-density organoclay-free invert emulsion drilling fluids (OCIEF). These fluids were formulated with acid-soluble manganese tetroxide as weighting agent and specially designed bridging-agent package. The fluids were hot rolled at 300°F and their filtration and rheological properties were measured. The paper describes the static-aging, contamination, HTHP rheology measurements and filter-cake breaking studies of the fluids at 300°F. Particle plugging experiments were performed on both the fluids in order to determine the invasion characteristics and the non-damaging nature of the fluids. These organoclay-free invert emulsion fluids were then field-trialed in different wells with good results. The OCIEFs showed optimum rheology and filtration properties. The fluids gave lower PV, which ensured that the fluid presents low ECD contribution while drilling/circulating. Sag factor analysis for the fluids after static aging for 24 and 48hours showed excellent stability and minimal sag propensity. HTHP rheology showed that the fluids had consistent PV and YP values across a range of temperatures and pressures. Contamination studies showed that the effect of contaminants on the organoclay-free fluid was minimal and any change in properties can be easily controlled using conventional treatments. The paper thus demonstrates the superior performance of the developed fluid in achieving the desired lab and field performance. Field deployment of the 95pcf organoclay-free invert emulsion fluid helped to maintain the required hole stability in the HTHP well. The well was displaced to 95pcf production screen test (PST) fluid and completed with a 4 ½" sand screen.

Utilization of drilled wells operations’ records is required to perform improvement of performance to minimize drilling cost of planned drilling of new and re-entry wells (workover – wells). Many operators are always interested in finding optimum ways to save on drilling cost. Optimization of Rate of Penetration (ROP) has direct effects on cost reduction. Different Techniques were used to optimize ROP such as regression technique, multiple linear regression technique, neural network, artificial neural network methods, and basic reference of Bayesian networks. There are several factors that will limit application of ROP optimization models in different hole sections with high degree of accuracy. It is the authors’ opinion that modeling on smaller selected section with controlled parameters will give better optimization and validation. In this paper an empirical correlation of rate of penetration (ROP) is presented for a particular hole section. The data selected are from same hole size, formation type and mud type. It is based on monitoring and controlling simultaneously the applied weight on bit (WOB), drill-string's rotation (RPM), Torque (TRQ) and rig pump's flow rate (GPM). During this study will demonstrate the use of this empirical correlation to improve drilling in a hole section by more than 50%. The developed model also has high potential to be automated in real time operating environment to improve drilling performance.

Determining the optimal configuration/ architecture for neural network could be a very time-consuming process. It includes going through big number of scenarios to simulate and validate different architecture. This paper will go over a process for intelligently automating this tedious task by providing a utility that will automatically validate the different permutation and picking the optimum configuration. This is done with maximum efficiency by utilizing parallel computing methods combined with in-memory tables to produce very fast result. The new methods enabled Exploration and Producing (E&P) data scientists to simulate and validate a big number of neural network configurations/ architectures in shorter time, allowing them to identify the optimum neural network configurations that can generate the best prediction results. Leading to more accurate Artificial Intelligent/ Machine Learning (AI/ML) models. To quantify this method, this paper approach can cut the time needed for such process from weeks to hours, especially when dealing with modeling E&P real-time problem. The process involved utilizing parallel computing methods combined with in-memory tables, not only in processing complex Artificial Intelligent/ Machine Learning (AI/ML) models, but also in developing the complex models. This approach is intended to empower data scientist in the Exploration and Producing field to quickly reach the most optimize model, leading to faster approach for safer, and more cost-effective solutions