A key role of drilling optimization is reducing the cost and non-productive time (NPT) for drilling operations. The rate of penetration (ROP) directly affects the overall cost and cost per foot of drilling operations and could lead to significant cost savings or expenses. Traditionally, empirical ROP modeling is used to predict bit response or estimate ROP using nearby offset data. Due to the complexity and nonlinearity of ROP, data-driven modeling, such as machine learning (ML), became more attractive. The objective of this paper is to develop an ROP data-driven artificial neural network (ANN) model using drilling and formation data collected from three nearby wells. Additionally, drilling optimization was conducted and compared with traditional empirical ROP models. The advantages and disadvantages of both methods are discussed, and the direction of future data-driven modeling is highlighted. The data-driven ANN model demonstrated strong performance when compared to the field data. The ANN model showed an RMSE and R² of 3.89 m/h and 0.93 for the training data and an RMSE and R² of 4.16 m/h and 0.92 for the testing dataset. The sensitivity analysis showed that the ANN model predicted higher ROP than the empirical models in the selected interval. Due to the limited bit wear data compared to the operational parameters, coupled simultaneous data-driven and empirical modeling is believed to be the future direction for data-driven drilling optimization.

With the majority of the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS)-sponsored earthen dams exceeding their planned service life, seepage is a concern since it is a common failure mechanism among earthen dams as a whole. Seepage occurs when water penetrates a hydraulic barrier to create open pathways, channels, or piping leading to internal erosion or stability issues due to increased effective stress. Due to the complex geometries of these hydraulic structures, time-dependent boundary conditions, and complicated failure analyses, numerical approaches have been used to estimate seepage. However, the existing numerical models are either computationally expensive or assume steady-state conditions. This work develops the framework for using a transient thermal finite element analysis (FEA) model as an analogy to predict transient seepage. The FEA model is compared to a transient analytical model to verify the approach. A sensitivity analysis of the FEA model shows that the time aspect of seepage flow is dependent on the medium, i.e., soil, and not on the fluid properties.

Wellbore leakage is a concern for abandoned oil and gas wells due to greenhouse gas emissions. The leakage mechanisms and resulting integrity are not well understood. Therefore, researchers have used analytical and numerical models to investigate wellbore integrity. An analytical solution, a finite element model without failure mechanisms, and a finite element model with failure criteria were developed and compared. The benefits and shortcomings of each model were discussed, and the different models were compared with three case study wells. The results of this work show that all three numerical models predict debonding between the cement sheath and the casing. However, including the failure criteria in the models proved to be critical in predicting correct stress distributions.

Over half of the approximately 12,000 earthen watershed dams sponsored by the USDA have exceeded their planned 50-year service life. Age, land use changes, extreme weather events, structural deterioration, and sedimentation filling flood pools pose increased risks of dam incidents and potential failures. Among various mechanisms leading to integrity issues, soil erosion is of particular concern due to its potential to occur with little warning. The objective of this research is to determine if soil erosion can be predicted using acoustic emissions. A simulated dam overtopping experiment was replicated in a test flume with dimensions of 0.61 m by 4.27 m (2 ft. by 14 ft.) with a 13.7% slope and a 0.15 m (6 in) layer of inorganic clay (USCS CL) compacted at 17.4% moisture content. A constant flow discharge of 0.07 m³/s (2.37 cfs) was applied to induce erosion. The test was performed until complete failure of the test section occurred. Throughout the experiment, a sonar radar, a 3D scanning total station, and an accelerometer were used to monitor the water level, erosion levels, and vibrations, respectively. The frequency analysis of the water-induced vibrations was compared to measured erosion volumes to determine if in situ vibrations can predict erosion. The results revealed a linear relationship between erosion volume and time, with noticeable changes in the frequency domains as erosion progressed. The outcomes of this research have the potential to provide real-time insights into the integrity of earthen dams concerning erosion, offering a valuable tool for monitoring and maintenance.