Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters.

The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models.

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification.

Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive.

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.

Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling.

Accurate prediction of formation tops and lithology plays a critical role in optimizing drilling processes, cost reduction, and risk mitigation in hydrocarbon operations. Although several techniques like well logging, core sampling, cuttings analysis, seismic surveys, and mud logging are available for identifying formation tops, they have limitations such as high costs, lower accuracy, manpower-intensive processes, and time or depth lags that impede real-time estimation. Consequently, this study aims to leverage machine learning models based on easily accessible drilling parameters to predict formation tops and lithologies, overcoming the limitations associated with traditional methods.

Data-driven models to predict shale wettability for CO sequestration applications.

The significance of CO wetting behavior in shale formations has been emphasized in various CO sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of machine learning (ML); artificial neural networks (ANN), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) tools to estimate the contact angle, a key indicator of shale wettability, providing a more efficient alternative to conventional laboratory methods.

Data-Driven Framework for Real-time Rheological Properties Prediction of Flat Rheology Synthetic Oil-Based Drilling Fluids.

Appropriate mud properties enhance drilling efficiency and decision quality to avoid incidents. The detailed mud properties are mainly measured in laboratories and are usually measured twice a day in the field and take a long time. This prevents real-time mud performance optimization and adversely affects proactive actions.

Estimation of rocks' failure parameters from drilling data by using artificial neural network.

Comprehensive and precise knowledge about rocks' mechanical properties facilitate the drilling performance optimization, and hydraulic fracturing design and reduces the risk of wellbore-related problems. This paper is concerned with the failure parameters, namely, cohesion and friction angle which are conventionally estimated using Mohr's cycles that are drawn using compressional tests on rock samples. The availability, continuity and representability, and cost of acquiring those samples are major concerns.

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).

Application of Machine Learning to Predict Estimated Ultimate Recovery for Multistage Hydraulically Fractured Wells in Niobrara Shale Formation.

The completion design of multistage hydraulic fractured wells including the cluster spacing injected proppant and slurry volumes has shown a great influence on the well production rates and estimated ultimate recovery (EUR). EUR estimation is a critical process to evaluate the well profitability. This study proposes the use of different machine learning techniques to predict the EUR as a function of the completion design including the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations.

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 Water Saturation in Tight Gas Sandstone Formation Using Artificial Intelligence.

Water saturation ( ) is a vital factor for the original oil and gas in place (OOIP and OGIP). Numerous available equations can be used to calculate , but their values have been unreliable and strongly depend on core analyses, which are costly and time-consuming. Hence, this study implements artificial intelligence (AI) modules to predict from the conventional well logs.

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.

Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs.

Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented.

Applications of Artificial Intelligence to Predict Oil Rate for High Gas-Oil Ratio and Water-Cut Wells.

Measuring oil production rates of individual wells is important to evaluate a well's performance. Multiphase flow meters (MPFMs) and test separators have been used to estimate well production rates. Due to economic and technical issues with MPFMs, especially for high gas-oil ratio (GOR) reservoirs, the use of a choke formula for estimating well production rate is still popular.