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

Bridging performances of lost circulation materials (LC-LUBE and mica) and their blending in 80/20 and 60/40 oil-based drilling fluids

In drilling wells, lost circulation, barite sagging, shale swelling, and formation damage are critical problems for the industry. These problems can be controlled by designing appropriate drilling fluids and lost circulation materials. In this study, the performance of 80/20 and 60/40 oil-based drilling fluids (OBMs) was compared based on the lost circulation materials’ (LCMs) bridging performance, filtrate loss, barite sagging, and shale stability. The results show that in terms of LCM stability, the performance of LC-LUBE improved when blended with mica. Both drilling fluids inhibit shale swelling. The overall analysis showed that the 60/40 OBM is better and recommended.publishedVersio

Real-Time Minimization of Mechanical Specific Energy with Multivariable Extremum Seeking

Drilling more efficiently and with less non-productive time (NPT) is one of the key enablers to reduce field development costs. In this work, we investigate the application of a data-driven optimization method called extremum seeking (ES) to achieve more efficient and safe drilling through automatic real-time minimization of the mechanical specific energy (MSE). The ES algorithm gathers information about the current downhole conditions by performing small tests with the applied weight on bit (WOB) and drill string rotational rate (RPM) while drilling and automatically implements optimization actions based on the test results. The ES method does not require an a priori model of the drilling process and can thus be applied even in instances when sufficiently accurate drilling models are not available. The proposed algorithm can handle various drilling constraints related to drilling dysfunctions and hardware limitations. The algorithm’s performance is demonstrated by simulations, where the algorithm successfully finds and maintains the optimal WOB and RPM while adhering to drilling constraints in various settings. The simulations show that the ES method is able to track changes in the optimal WOB and RPM corresponding to changes in the drilled formation. As demonstrated in the simulation scenarios, the overall improvements in rate of penetration (ROP) can be up to 20–170%, depending on the initial guess of the optimal WOB and RPM obtained from e.g., a drill-off test or a potentially inaccurate model. The presented algorithm is supplied with specific design choices and tuning considerations that facilitate its simple and efficient use in drilling applications.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.publishedVersio

Improving drilling hydraulics estimations ‑ a case study

Accurate pressure drop estimation is important for drill string and bit nozzles design and optimized fluid circulations as well as identifying the drilling problems such as bit nozzle(s) washout or plugging. In this study, the Bingham Plastic model has been modified by applying a coefficient to its turbulent pressure loss calculations. This coefficient encompasses the effects of the drill pipe tool joints and other effects in estimation of pressure losses. The range of the coefficient was determined in field applications for different hole sizes and mud types. The results showed that applying a correction coefficient of 1.08–1.12 to turbulent pressure loss equations (depending on borehole size and mud type) improves the pressure loss estimation. By applying this coefficient, the estimated pressure losses are increased to compensate the under-estimation of the Bingham Plastic model. This is considered a significant contribution to accurate calculation of borehole hydraulics and in-time detection and identification of borehole problems and reduction of invisible lost time. The findings also showed that this enhanced effect is independent of the mud type. The use of this coefficient removes the necessity of using rather complex mud rheological models such as the Herschel–Bulkley model.publishedVersio

Rate of penetration optimization using moving horizon estimation

Increase of drilling safety and reduction of drilling operation costs, especially improvement of drilling efficiency, are two important considerations in the oil and gas industry. The rate of penetration (ROP, alternatively called as drilling speed) is a critical drilling parameter to evaluate and improve drilling safety and efficiency. ROP estimation has an important role in drilling optimization as well as interpretation of all stages of the well life cycle. In this paper, we use a moving horizon estimation (MHE) method to estimate ROP as well as other drilling parameters. In the MHE formulation the states are estimated by a forward simulation with a pre-estimating observer. Moreover, it considers the constraints of states/outputs in the MHE problem. It is shown that the estimation error is with input-to-state stability. Furthermore, the ROP optimization (to achieve minimum drilling cost/drilling energy) concerning with the e cient hole cleaning condition and downhole environmental stability is presented. The performance of the methodology is demonstrated by one case study.publishedVersio

Rate of Penetration Optimization using Moving Horizon Estimation

Increase of drilling safety and reduction of drilling operation costs, especially improvement of drilling efficiency, are two important considerations in the oil and gas industry. The rate of penetration (ROP, alternatively called as drilling speed) is a critical drilling parameter to evaluate and improve drilling safety and efficiency. ROP estimation has an important role in drilling optimization as well as interpretation of all stages of the well life cycle. In this paper, we use a moving horizon estimation (MHE) method to estimate ROP as well as other drilling parameters. In the MHE formulation the states are estimated by a forward simulation with a pre-estimating observer. Moreover, it considers the constraints of states/outputs in the MHE problem. It is shown that the estimation error is with input-to-state stability. Furthermore, the ROP optimization (to achieve minimum drilling cost/drilling energy) concerning with the efficient hole cleaning condition and downhole environmental stability is presented. The performance of the methodology is demonstrated by one case study

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

MoS2 Nanoparticle Effects on 80°C Thermally Stable Water-Based Drilling Fluid

Bentonite-based drilling fluids are used for drilling, where inhibitive fluids are not required. The rheological and the density properties of the drilling fluids are highly affected by high temperature and pressure. Due to high temperature, the clay particles stick together, and the fluid system becomes more flocculated. Poorly designed drilling fluid may cause undesired operational issues such as poor hole cleaning, drill strings sticking, high torque and drag. In this study, the 80 °C thermally stable Herschel Bulkley’s and Bingham plastic yield stresses drilling fluids were formulated based on lignosulfonate-treated bentonite drilling fluid. Further, the impact of a MoS2 nanoparticle solution on the properties of the thermally stable base fluid was characterized. Results at room temperature and pressure showed that the blending of 0.26 wt.% MoS2 increased the lubricity of thermally stable base fluid by 27% and enhanced the thermal and electrical conductivities by 7.2% and 8.8%, respectively.publishedVersio

Numerical investigation of the impacts of borehole breakouts on breakdown pressure

Borehole breakouts appear in drilling and production operations when rock subjected to in situ stress experiences shear failure. However, if a borehole breakout occurs, the boundary of the borehole is no longer circular and the stress distribution around it is different. So, the interpretation of the hydraulic fracturing test results based on the Kirsch solution may not be valid. Therefore, it is important to investigate the factors that may affect the correct interpretation of the breakdown pressure in a hydraulic fracturing test for a borehole that had breakouts. In this paper, two steps are taken to implement this investigation. First, sets of finite element modeling provide sets of data on borehole breakout measures. Second, for a given measure of borehole breakouts, according to the linear relation between the mud pressure and the stress on the borehole wall, the breakdown pressure considering the borehole breakouts is acquired by applying different mud pressure in the model. Results show the difference between the breakdown pressure of a circular borehole and that of borehole that had breakouts could be as large as 82% in some situations.publishedVersio