Closed-loop well construction optimization (CLWCO) using stochastic approach under time uncertainty

There is a digital step change taking place in well construction today. More and better data will become available for a vast number of analyses. The well construction process is complicated and includes several hundred parameters. There are many inhouse drilling analytics tools used by service and consulting companies. The objective of this paper is to aim at a complete time optimization and to improve health, safety and the environment (HSE) in a time-effective way. In this paper we establish and apply a full approach methodology for closed Loop well construction optimization (CLWCO) under time uncertainty. CLWCO involves six major steps: data gathering,a work-breakdown structure (WBS) in drilling scenarios, time estimation (budget time &technical time),time simulation (MCS&PERT), scenario analysis & optimization and finally updating time model. CLWCO involves three major concepts: optimizing the time plan based on current time knowledge, drilling new wells and collecting time data, finally updating multiple time models based on all of the available data. In the CLWCO step, work breakdown structure (W.B.S), time and controls for new wells are optimized by Monte-Carlo Simulation and program evaluation review technique (PERT). This paper goals are to identify and in best case quantify “the value of Monte Carlo simulation and Program Evaluation Review Technique (PERT) in batch & conventional time drilling optimization” in offshore wells for clients or operating company. Batch drilling does not combine professionally with modern techniques yet.we fill this gap by using modern techniques to optimize and enhance drilling work. We evaluate and analysis above-mentioned approach for batch drilling which has become increasingly prevalent in the petroleum industry as large and small investors alike seek to increase their profit margin. The insight of many of these oil and gas companies was to drill and complete wells using new techniques with the desire of considerable reduction in drilling time and cost for the field. when similar hole sections such as 32″,24″,16″,12 ¼″ and 8 ½″ of different wells were drilled one after the other efficiency and profits would be greatly increased. According to obtained results in closed loop well construction optimization (CLWCO), these methods are successful as it needs less time and cost to drill a lot of wells using the same platform. we simulated a drilling program for the case study of SP field by Monte-Carlo Simulation and program evaluation review technique (PERT),at the end we propose the optimum probable time to do future drilling program in SP field. The time versus depth graph of drilling project show that the improved drilling efficiency for drilling project designed as 11 wells would reduce the total drilling time around 15% in compare of previous drilling projects in phase SP6,SP7 and SP8,totally average drilling time have been improved between 2.5 and 8 days in MCS and PERT simulation technique for each well by using CLWCO.We presented the optimal plan coupling with batch drilling could be implemented in the future phases of SP field, which has resulted in decreasing drilling time to 30 days by using casing-drilling and liner-drilling technology.acceptedVersio

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices

Controlling reservoir fluid flow is important for maximizing petroleum production through wellbores. A major challenge that reduces the production of oil is early breakthrough of secondary fluids to the wellbore perforations. This occurs due to the low viscosity of gas and water relative to oil, and the heterogeneity of reservoir permeability. Autonomous inflow control devices represent a new self-regulating technology that helps to increase petroleum production, particularly oil, by restricting the production of unwanted fluids like gas and water into the wellbores. This study develops smart systems based on machine learning models to predict the performance of autonomous inflow control devices. Several machine learning models are evaluated including adaptive neuro fuzzy inference system, hybrid adaptive neuro-fuzzy inference system genetic algorithm, artificial neural network and support vector machine and their prediction performance is compared to that of linear regression, full quadratic regression model and the mathematical autonomous inflow control device performance model. Each model is developed to estimate the differential pressure of Equiflow autonomous inflow control devices based on ninety experimentally recorded data records. The range of equiflow autonomous inflow control device, viscosity, density and flow rate are the input variables and differential pressure is the output dependent variable of each model. The prediction accuracy of the models is assessed in terms of several standard statistical accuracy performance measures. These performance indicators confirm that the machine-learning models provide superior prediction accuracy for autonomous inflow control device differential pressure. Overall, the support vector machine achieves the most accurate predictions of all the models evaluated recording root mean square error of 0.14 Mpa and coefficient of determination of 0.98. On the other hand, the linear regression model records the lowest prediction performance, highlighting the non-linearity of the autonomous inflow control device processes.publishedVersio

Optimizing the separation factor along a directional well trajectory to minimize collision risk

Optimizing the trajectory of directional wellbores is essential to minimize drilling costs and the impacts of potential drilling problems. It poses multi-objective optimization challenges. Well-design optimization models initially focus on wellbore-length minimization, but ideally also need to consider minimizing the surface torque during drilling and address, among other constraints, collision avoidance with offset wells. A novel trajectory-optimization model is described that computes the separation factor along the wellbore. It employs a genetic optimization algorithm with an objective function that maximizes the minimum separation factor along the entire length of a wellbore. Plausible well trajectories are identified within a feasible solution space defined by user-identified constraints. The simplicity and effectiveness of the proposed model are demonstrated using a case study involving real well data from the Reshadat oil field offshore southern Iran. In the case considered, a proposed well trajectory is identified as unsafe in terms of its minimum separation factor with an offset well and is re-planned with the proposed model to achieve a safer trajectory.publishedVersio

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices

Controlling reservoir fluid flow is important for maximizing petroleum production through wellbores. A major challenge that reduces the production of oil is early breakthrough of secondary fluids to the wellbore perforations. This occurs due to the low viscosity of gas and water relative to oil, and the heterogeneity of reservoir permeability. Autonomous inflow control devices represent a new self-regulating technology that helps to increase petroleum production, particularly oil, by restricting the production of unwanted fluids like gas and water into the wellbores. This study develops smart systems based on machine learning models to predict the performance of autonomous inflow control devices. Several machine learning models are evaluated including adaptive neuro fuzzy inference system, hybrid adaptive neuro-fuzzy inference system genetic algorithm, artificial neural network and support vector machine and their prediction performance is compared to that of linear regression, full quadratic regression model and the mathematical autonomous inflow control device performance model. Each model is developed to estimate the differential pressure of Equiflow autonomous inflow control devices based on ninety experimentally recorded data records. The range of equiflow autonomous inflow control device, viscosity, density and flow rate are the input variables and differential pressure is the output dependent variable of each model. The prediction accuracy of the models is assessed in terms of several standard statistical accuracy performance measures. These performance indicators confirm that the machine-learning models provide superior prediction accuracy for autonomous inflow control device differential pressure. Overall, the support vector machine achieves the most accurate predictions of all the models evaluated recording root mean square error of 0.14 Mpa and coefficient of determination of 0.98. On the other hand, the linear regression model records the lowest prediction performance, highlighting the non-linearity of the autonomous inflow control device processes.Cited as: Yavari, H., Khosravanian, R., Wood, D. A., Aadnoy, B. S. Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices. Advances in Geo-Energy Research, 2021, 5(4): 386-406, doi: 10.46690/ager.2021.04.0

An approach for optimization of controllable drilling parameters for motorized bottom hole assembly in a specific formation

This study focuses on optimizing drilling parameters when using Positive Displacement Motors (PDMs). In drilling operations involving mud motors, weight-on-bit (WOB) alterations lead to variations in the system's parasitic pressure drop. Consequently, this affects the optimum flow rate and the hydraulic power of the bit. Also, if the flow rate changes, the bit's rotations per minute (RPM) also change. In other words, using PDMs creates a link between the hydraulic system and the drilling speed, such that changing drilling parameters such as the WOB causes changes in the hydraulic system's performance. Therefore, one possible way to optimize the drilling parameters is to consider the drilling rate and hydraulic system simultaneously using a multi-objective approach. This study used an integrated approach encompassing data mining and mathematical modeling, employing a multi-objective framework to identify optimal parameters. The approach was applied to Dariyan Formation drilling data. The data mining approach revealed a well-distributed data set covering optimal and suboptimal zones suitable for optimization. In data mining, the identification of optimal conditions included a WOB of 11500 lb, a rotation speed of 105.8 rev/min, and a flow rate of 843 gpm, leading to an ROP of 44.23 ft/h. In multi-objective optimization, the optimal parameters consisted of a WOB of 14480 lb, a rotation speed of 115 rev/min, and a flow rate of 920.8 gpm, resulting in an ROP of 40.49 ft/h. Comparing optimal results with the drilling data shows a substantial MSE reduction of over 35 %. The results show the good performance of this approach in detecting the optimal and non-optimal drilling variables