Stochastic Assessment of Groundwater Contamination Risks From Onshore Gas Development Using Computationally Efficient Analytical and Numerical Transport Models

Regulators require the gas industry to assess the risks of unintentional release of chemicals to the environment and implement measures to mitigate it. Industry standard models for contaminant transport in aquifers do not explicitly model processes in the unsaturated zone and groundwater models often require long run times to complete simulation of complex processes. We propose a stochastic numerical-analytical hybrid model to overcome these two shortcomings and demonstrate its application to assess the risks associated with onshore gas drilling in the Otway Basin, South Australia. The novel approach couples HYDRUS-1D to an analytical solution to model contaminant transport in the aquifer. Groundwater velocities and chemical trajectories were derived from a particle tracking analysis. The most influential parameters controlling solute delivery to the aquifer were the soil chemical degradation constant and the hydraulic conductivity of a throttle soil horizon. Only 18% of the flow paths intercepted environmental receptors within a 1-km radius from the source, 87% of which had concentrations of <1% of the source. The proposed methodology assesses the risk to environmental assets and informs regulators to implement measures that mitigate risk down to an acceptable level.

Development of well complexity calculator and its integration into standard well engineering management system/well delivery system

Oil and gas well drilling is the most important and complex task for oil and gas exploration. It is not necessary that design and execution complexity remain the same for two different wells even in the same field. It is possible to have a very complex well to drill after a very straightforward simple well being drilled earlier in the same field. Making correlation or comparison of any of the two or more than two oil and gas drilling wells is an ongoing debate in the petroleum industry. Generally, companies compare the oil and gas drilling wells on a single or two parameters, for example: time versus depth, directional trajectories, well cost and/or other single factors in disengagement of one another. In order to compare two different types of oil and gas drilling wells, having distinctive design, drilling and fluid program and challenges, a scientific rating system is required, which can relate various wells with one another. In this research paper, a calculator named Well Complexity Calculator has been developed to measure the complexity of the oil and gas well drilling by using different parameters. All these parameters are commonly affecting the drilling program and its execution. Secondly, a methodology is designed for integration of Well Complexity Calculator into standard Well Engineering Management System/Well Delivery System for better execution of drilling program. Fifty-one (51) oil and gas drilling well complexity parameters have been utilized to develop Well Complexity Calculator, where they are categorized into three main complexities types named Design Well Complexity, Geological Well Complexity and Project Well Complexity. Design and Geological Well Complexities combine to form Drilling Well Complexity, and then Drilling Well Complexity and Project Well Complexity combine to form Well Complexity. Median, Mode and Monte Carlo simulation techniques were chosen to develop the calculator where Median showed best suited results and was accordingly chosen for the final calculator. Sixty-six (66) actual oil and gas wells’ camouflaged drilling data were used to analyze and fine tune the developed Well Complexity Calculator. Output complexities of these wells were falling in different complexity levels. Moreover, it was seen that the number of low, high and medium complexity wells was different for Design, Geological, Project, Drilling and Well Complexities which is in line with the real-world scenario.The findings and the output Well Complexity Calculator can be very useful at any stage from initial planning to close-out of a well. Without the application of a system like Well Complexity Calculator, wells are categorized as low, medium or high complexity based on either two to three major parameters or based on qualitative assessment of team involved in the project. Here, step-by-step procedure is developed and explained by which any company involved in Drilling and Well Operations can develop their own Well Complexity Calculator and then accordingly integrate it into their Well Engineering Management System/Well Delivery System.

Drilling Automation: The Step Forward for Improving Safety, Consistency, and Performance in Onshore Gas Drilling

In the current oil and gas drilling industry, the modernization of rig fleets has been shifting toward high mobility, artificial intelligence, and computerized systems. Part of this shift includes a move toward automation. This paper summarizes the successful application of a fully automated workflow to drill a stand, from slips out to slips back in, in a complex drilling environment in onshore gas. Repeatable processes with adherence to plans and operating practices are a key requirement in the implementation of drilling procedures and vital for optimizing operations in a systematic way. A drilling automation solution has been deployed in two rigs enabling the automation of both pre-connection and post-connection activities as well as rotary drilling of an interval equivalent to a typical drillpipe stand (approximately 90 ft) while optimizing the rate of penetration (ROP) and managing drilling dysfunctionalities, such as stick-slip and drillstring vibrations in a consistent manner. So far, a total of nine wells have been drilled using this solution. The automation system is configured with the outputs of the drilling program, including the drilling parameters roadmap, bottomhole assembly tools, and subsurface constraints. Before drilling every stand, the driller is presented with the planned configuration and can adjust settings whenever necessary. Once a goal is specified, the system directs the rig control system to command the surface equipment (draw works, auto-driller, top drive, and pumps). Everything is undertaken in the context of a workflow that reflects standard operating procedures. This solution runs with minimal intervention from the driller and each workflow contextual information is continuously displayed to the driller thereby giving him the best capacity to monitor and supervise the operational sequence. If drilling conditions change, the system will respond by automatically changing the sequence of activities to execute mitigation procedures and achieve the desired goal. At all times, the driller has the option to override the automation system and assume control by a simple touch on the rig controls. Prior to deployment, key performance indicators (KPI), including automated rig state-based measures, were selected. These KPIs are then monitored while drilling each well with the automation system to compare performance with a pre-deployment baseline. The solution was used to drill almost 60,000 ft of hole section with the system in control, and the results showed a 20% improvement in ROP with increased adherence to pre-connection and post-connection operations. Additionally, many lessons were learned from the use and observation of the automation workflow that was used to drive continuous improvement in efficiency and performance over the course of the project. This deployment was the first in the region and the system is part of a comprehensive digital well construction solution that is continuously enriched with new capabilities. This adaptive automated drilling solution delivered a step change in performance, safety, and consistency in the drilling operations.

A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang

In gas drilling operations, the rate of penetration (ROP) parameter has an important influence on drilling costs. Prediction of ROP can optimize the drilling operational parameters and reduce its overall cost. To predict ROP with satisfactory precision, a stacked generalization ensemble model is developed in this paper. Drilling data were collected from a shale gas survey well in Xinjiang, northwestern China. First, Pearson correlation analysis is used for feature selection. Then, a Savitzky-Golay smoothing filter is used to reduce noise in the dataset. In the next stage, we propose a stacked generalization ensemble model that combines six machine learning models: support vector regression (SVR), extremely randomized trees (ET), random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGB). The stacked model generates meta-data from the five models (SVR, ET, RF, GB, LightGBM) to compute ROP predictions using an XGB model. Then, the leave-one-out method is used to verify modeling performance. The performance of the stacked model is better than each single model, with R2 = 0.9568 and root mean square error = 0.4853 m/h achieved on the testing dataset. Hence, the proposed approach will be useful in optimizing gas drilling. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the relevant ROP parameters.

Benzene Exposure on Rig X from Drilling Fluid and Effectiveness of Local Exhaust Ventilation

Introduction: Benzene is a carcinogenic compound commonly found in drilling fluid, a chemical used in oil and gas drilling operations. Benzene exposure to workers is known to cause acute and/or chronic disease. Adequate control measures shall be identified and implemented to prevent the adverse health effects of benzene from the utilization of drilling fluid. Methods: This study measured benzene concentrations at several locations, above the drilling rig, which has the potential risk of benzene vapor exposure. From the measurement results, if the threshold limit value was exceeded, LEV was proposed to be installed and the effectiveness of LEV at each location would be assessed. A two-tailed t-test was used with a confidence level of 95% (α=0.05) to measure the effectiveness of LEV. Results: In several areas, benzene concentration exceeded TLV-TWA, and LEV was installed in those areas as control measures. In this study, it was found that LEV was not always effective in reducing the concentration of benzene in some areas. Conclusion: Drilling fluid was essential for drilling activity, and this could cause benzene vapor to contaminate the working area. The installation of the LEV shall consider the type of containment through which the drilling fluid flows to ensure the mitigation measures are effective to reduce the concentration of benzene in the air that may be exposed to workers.Keywords: benzene, drilling fluid, exhaust ventilation