Impact of Eco-Friendly Drilling Additives on Foaming Properties for Sustainable Underbalanced Foam Drilling Applications.

Underbalanced foam drilling stands out as a drilling technique acclaimed for its capacity to enhance safety and efficiency in operations. Utilizing foams as drilling fluids offers several benefits over traditional methods, including lower density, diminished formation damage, and augmented borehole stability. However, the persistent challenge of sustaining foam stability in demanding conditions, particularly amid elevated water salinity and alkaline environments, remains a critical issue.

Impact of Pressure and Temperature on Foam Behavior for Enhanced Underbalanced Drilling Operations.

Foam, a versatile underbalanced drilling fluid, shows potential for improving the drilling efficiency and reducing formation damage. However, the existing literature lacks insight into foam behavior under high-pH drilling conditions. This study introduces a novel approach using synthesized seawater, replacing the conventional use of freshwater on-site for the foaming system's liquid base.

Application of Silicomanganese Fume as a Novel Bridging Material for Water-Based Drilling Fluids.

Steelmaking industrial waste, that is, silicomanganese fume (SMF), is one of the byproducts obtained during the steelmaking process in an electric submerged arc furnace at 1500 °C. Millions of tons of such wastes are generated yearly and used in different applications such as road construction, cement mortar, recycling into sinter plant, and so forth. In this study, the application of SMF in the drilling operations was investigated by employing SMF as a bridging material (BM) in water-based drilling fluid (WBF).

A hybrid data-driven solution to facilitate safe mud window prediction.

Safe mud window (SMW) defines the allowable limits of the mud weights that can be used while drilling O&G wells. Controlling the mud weight within the SMW limits would help avoid many serious problems such as wellbore instability issues, loss of circulation, etc. SMW can be defined by the minimum mud weight below which shear failure (breakout) may occur (MW) and the maximum mud weight above which tensile failure (breakdown) may occur (MW).

New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data.

The maximum (Sh) and minimum (Sh) horizontal stresses are essential parameters for the well planning and hydraulic fracturing design. These stresses can be accurately measured using field tests such as the leak-off test, step-rate test, and so forth, or approximated using physics-based equations. These equations require measuring some geomechanical parameters such as the static Poisson ratio and static elastic modulus via experimental tests on retrieved core samples.

Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application.

The least principal stresses of downhole formations include minimum horizontal stress ( ) and maximum horizontal stress ( ). and are substantial parameters that significantly affect the design and optimization of the drilling process. These stresses can be estimated using theoretical equations in addition to some field tests, i.

Machine learning application to predict in-situ stresses from logging data.

Determination of in-situ stresses is essential for subsurface planning and modeling, such as horizontal well planning and hydraulic fracture design. In-situ stresses consist of overburden stress (σ), minimum (σ), and maximum (σ) horizontal stresses. The σ and σ are difficult to determine, whereas the overburden stress can be determined directly from the density logs.

Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks.

High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV).