Non-productive Time Research Articles

Machine Learning Aids Early Detection of Stuck Pipe in Extended-Reach Wells

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 206516, “Stuck-Pipe Early Detection in Extended-Reach Wells Using Ensemble Method of Machine Learning,” by Rushad Ravilievich Rakhimov, SPE, Schlumberger, and Oleg Valerievich Zhdaneev, SPE, and Konstantin Nikolaevich Frolov, SPE, Ministry of Energy of the Russian Federation, et al. The paper has not been peer reviewed. The objective of this paper is to describe the experience of using a machine-learning model prepared by the ensemble method to prevent stuck-pipe events during construction of extended-reach wells. The tasks performed include collecting, analyzing, and cleaning historical data; selecting and preparing a machine-learning model; and testing it on real-time data by means of a desktop application. The idea is to display the solution at the rig floor, allowing the driller to take actions quickly for prevention of stuck-pipe events. Problem Analysis Packoff is the blockage of the annular space with drilled cuttings or parts of rocks fallen from wellbore walls. In a packoff situation, circulation is severely limited or impossible. With an inadequate response to packoff, fracturing of rocks below the bottomhole assembly (BHA) can occur, which leads to losses of drilling fluid and possible kick. In addition to time spent and technological risks, high expense is possible. In a worst-case scenario, the BHA can be left in hole and the bore must be redrilled, leading to a multimillion-dollar financial loss caused by nonproductive time. Packoff events fall mainly into two categories: - Caused by a cuttings bed that occurs when drilled cuttings are caught up while backreaming out of hole too promptly - Caused by wellbore collapse because of insufficient mud properties During the past 10 years, 28 wells have been drilled in the field of interest. Two hundred and twenty-seven incidents associated with stuck-pipe events have been registered. The light cases required an additional rereaming of the problematic interval, with a loss of time of up to 20 minutes. The most severe cases resulted in multimillion-dollar financial losses because of the BHA being lost in hole and the time spent on redrilling the wellbore. Classification of the incidents showed that 24 cases (11%) belong to the differential type of sticking, while 17 cases (8%) belong to the wellbore-geometry type and 186 cases (81%) to packoff events. The data collected at the field show that packoffs were encountered only during backreaming operations. Packoffs evolve rapidly. Thus, in most cases on the studied field, the time interval between trouble-free operation and sticking events is between 20 and 90 seconds (Fig. 1). Unlike physical modeling methods, machine-learning methods serve as a base for development of tools that work solely from historical data, allowing a more-precise simulation of real parameter behavior. Another advantage of machine-learning methods is the ability to select only the best historical cases for use. However, the use of historical data for prediction of downhole accidents in the well-construction process is not a novel approach. It has been used in the industry for more than 30 years. The complete paper includes an extensive discussion of examples in the literature.

Case Study: Not Just for Drilling: A Survey of the Application of Managed-Pressure Techniques

Every activity within a well alters the pressure being exerted on the open hole, and every mitigation technique attempts to maintain the desired pressure within acceptable limits. If the pressure becomes too low, kicks, borehole breakouts, and hole collapse are the primary consequences (Fig. 1). Pressure that is too high can damage the reservoir, induce fluid losses, and slow down operations. When these unfortunate events occur, remedial actions are usually necessary, increasing safety exposure, nonproductive time, and the overall costs of the project. To mitigate these concerns, operators have used the techniques of managed-pressure drilling (MPD) to maintain annular pressure and create a pressure-tight barrier against drilling hazards and manage inflow from the formation during drilling. While conventional drilling uses the hydrostatic pressure of the drilling mud to manage the pressure of the wellbore, MPD uses a combination of surface pressure, hydrostatic pressure, and annular friction to balance the exposed formation. Over the decades, MPD has been associated as a technology that is used only on problematic wells and only as a last resort. However, recent developments have highlighted that managing pressure is not just for drilling operations or just the most challenging wells. When integrated at the beginning of operations as part of a comprehensive well plan, managing pressure becomes a performance-enhancing solution for any type of well classification, including development, directional, multilaterals, and horizontals. Wellbore stability is maintained throughout the entire operation and pressure is dynamically altered in the annulus, enabling any operation to become faster with fewer challenges while delivering a more productive well and reducing overall costs and exposure to hazardous risks. The managed-pressure approach has even been leveraged to fully optimize a field/reservoir development program. An example of how MPD techniques can be incorporated into an overall well plan occurred on an ultradeepwater exploration well drilled by TotalEnergies in the Mexican waters of the Gulf of Mexico (SPE 200503). The operator and the service providers wanted to manage the pressure during the entire well program—including drilling, tripping, running casing, and cementing—to address pore pressure uncertainty, pressure ramp increase, and a narrow pore pressure/fracture gradient (PP/FG) window. The seafloor rested under 10,748 ft (3276 m) of water, and with the exploratory nature of the well, the conventional solution involved an excessive number of casing strings and an overbalanced mud weight (MW). Integrating MPD techniques enabled the operator to adjust the bottomhole pressure instantaneously, the result being a recognition that conventionally cementing a string of 13⅜ -in. casing to isolate the critical formation and safely continue drilling further stages of the well was impractical. To make the situation even more challenging, the engineers did not precisely know the size of the hole. A collaboration between the engineering teams of the operator, the cementing service provider, and MPD professionals resulted in a generated plan. A tail slurry of 15.86 ppg (1.90 SG) followed a 12.52 ppg (1.50 SG) lead gas-tight slurry. At a total depth of 13,622 ft (4152 m), the combination kept the equivalent circulating density (ECD) at 9.18 ppg (1.10 SG) without exceeding 9.51 ppg (1.14 SG). At the casing shoe, located at 12,801 ft (3902 m), the plan called for a 9.01 ppg (1.08 SG) without exceeding 9.35 ppg (1.12 SG), as shown in Fig. 2.

Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud.

The drilling fluid rheology is a critical parameter during the oil and gas drilling operation to achieve optimum drilling performance without nonproductive time or extra remedial operation cost. The close monitoring for rheological properties will help the drilling fluid crew to take quick actions to maintain the designed profiles for the drilling fluid rheology, especially when it comes to the flat rheology drilling fluid system, which is a new generation for harsh and specific drilling conditions that require flat profiles for the mud rheology regarding the temperature condition changes. The current study introduces a machine learning application toward predicting the rheology of synthetic oil-based mud (flat rheology type) for the full automation system of monitoring the mud rheological properties. Four models are developed, for the first time, to determine the rheological characteristics of flat rheology synthetic oil-based system using artificial neural networks. The developed models are capable of predicting the plastic and apparent viscosities, yield point, and flow behavior index from only the mud density and Marsh funnel as model inputs. The proposed models were trained and optimized from a real field dataset (369 measurements) with further testing the models using an unseen dataset of 153 data points. The predicted rheological properties achieved a high degree of accuracy versus the actual measurements and showed a coefficient of correlation range from 0.91 to 0.97 with an average absolute percentage error of less than 9.66% during the training and testing phases. Besides, machine learning-based correlations are proposed for estimating the rheological properties on the rig site without running the machine learning system for easy field applications.

Drilling Time Statistical Analysis and Benchmarking for Drilled Wells of Zubair Field

Activities of drilling wells are widely continued and increased all over the world either to develop the mature reservoirs or to discover new reserves. The majority of these wells experience numerous issues during drilling operations, resulting in financial losses and wasted time. This is referred to as non-productive time (NPT). Due to these difficulties, all of these concerns present a challenge to not only the drilling engineer or designer but also the company as well. All parties want also to keep the continuous improvement of their operations. Therefore, this paper aims to analyze most of the drilling issues appeared during drilling Zubair wells which are located in the Eastern Iraq. Furthermore, drilling time statistical study is done to differentiate between the various causes of Zubair wells' problems. A rate of penetration (ROP) benchmarking study is also done so as to determine the drilling performance relative to the best well. A comparison is performed to know the best well performance.

Dynamically Evaluating the Performance of Naturally Occurring Additives to Control Lost Circulation: On the Effect of Lost Circulation Material Type, Particle-Size Distribution, and Fracture Width

Summary Lost circulation is one of the most challenging problems during drilling of oil and gas wells. This issue leads to significant loss of drilling fluid, increase of nonproductive time as well as dictating additional costs to drilling companies. Lost circulation may also lead to other consequences, including stuck pipe, poor hole cleaning, and well control issues. How to efficiently control lost circulation have been traditionally depending on the type of the used lost circulation material (LCM). Injection of commonly available materials (without any further process on their chemical properties) into the thief zone is a common method of lost circulation control. These nonmodified materials are named as conventional LCMs against the unconventional LCMs which are designed/produced just for fluid lost control. The objective of this paper is to comparatively investigate the performance of cane, oak shell, wheat, and mica as LCM of water-based drilling fluid exposed to fractured formations. These materials were chosen because of their low cost, easy access, and compatibility with the environment. The sealing efficiency of these materials was assessed at different particle-size distributions (PSDs) for proper treatment of loss circulation. To do so, an experimental setup containing a cell with adjustable fracture size was designed. Among the LCM formulations made of each of the materials, oak shell formulations are better than the others, followed by mica and cane blends, respectively. The results reveal that combining the materials together is a better treatment than the separate use of them. As it will be seen in detail later, high diversity in particle size (broad PSD) causes more efficient control of fluid loss. Also, to reduce the dependency of sealing ability of LCM formulation on fracture size, mixing of the materials with different particle sizes and shapes is recommended.

Development of Artificial Intelligence Models for Estimating Rate of Penetration in East Baghdad Field, Middle Iraq

It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy inference system and genetic algorithm. An offset field data was collected from mud logging and wire line log from East Baghdad oil field south region to build the AI models, including datasets of two wells: well 1 for AI modeling and well 2 for validation of the obtained results. The types of interesting formations are sandstone and shale (Nahr Umr and Zubair formations). Nahr Umr and Zubair formations are medium –harder. The prediction results obtained from this study showed that the ANN technique can predict the ROP with high efficiency as well as FIS technique could achieve reliable results in predicting ROP, but GA technique has shown a lower efficiency in predicting ROP. The correlation coefficient and RMSE were two criteria utilized to evaluate and estimate the performance ability of AI techniques in predicting ROP and comparing the obtained results. In the Nahr Umr and Zubair formations, the obtained correlation coefficient values for training processes of ANN, FIS and GA were 0.94, 0.93, and 0.76 respectively. Data sets from another well (well 2) in the same field of interest were utilized to validate of the developed models. Datasets of well 2 were conducted against sandstone and shale formations (Nahr Umr and Zubair formations). The results revealed a good matching between the actual rate of penetration values and the predicted ROP values using two artificial intelligence techniques (neural network, and fuzzy inference technique). In contrast, the genetic algorithm model showed overestimation/ underestimation of the rate of penetration against sandstone and shale formations. This means that the optimum prediction of rate of penetration can be obtained from neural network model rather than using genetic algorithm and genetic algorithm techniques. The developed model can be successfully used to predict the rate of penetration and optimize the drilling parameters, achieving reduce the cost and time of future wells that will be drilled in the East Baghdad Iraqi oil field.

Modeling of Swab and Surge Pressures: A Survey

Swab and surge pressure fluctuations are decisive during drilling for oil. The axial movement of the pipe in the wellbore causes pressure fluctuations in wellbore fluid; these pressure fluctuations can be either positive or negative, corresponding to the direction of the movement of the pipe. For example, if the drill string is lowering down in the borehole, the drop is positive (surge pressure), and if the drill string is pulling out of the hole, the drop is negative (swab pressure). The intensity of these pressure fluctuations depends on the speed of the lowering down (tripping in) or withdrawing the pipe out (tripping out). High tripping speed corresponds to higher pressure fluctuations and can lead to fracturing the well formation. Low tripping speed leads to a slow operation, causing non-productive time, thus increasing the overall well budget. Researchers used mathematical equations and physics to understand the phenomena and have provided many empirical, mathematical, and physics-based models. This paper starts with a literature study on the swab and surge pressures. After that, this paper concludes with a proposal for a new approach. The new approach proposes developing new models that are more robust, using field data, as we have access to field data from drilling operations. Research using field data would provide data-driven methodologies as new solutions for the rate of penetration, reservoir management, and drilling optimization. The expected outcome will improve the performance of the tripping in and tripping out process within drilling and well construction, and will further reduce the risk related to swab and surge pressures.

Evaluation of the point-of-care devices KetoSureTM and StatStrip Express® blood ketone tests using β-hydroxybutyrate spiked samples

Measurement of β-hydroxybutyrate (BHB) in blood is used clinically as a measure of ketosis when diabetic ketoacidosis is expected. With the introduction of point-of-care testing (POCT) for blood-BHB, nonproductive time is reduced to a minimum in a potential critical situation; however, studies have observed inferior quality of POCT-BHB. Recently, the POCT device KetoSure (Roche) has been introduced to the clinic. In this study, we evaluated the imprecision and linearity of KetoSure and compared this to the established StatStrip Express (Nova Biomedical) based on spiked full blood samples. We found comparable imprecision for KetoSure and StatStrip Express. However, linearity was only observed in the lower part of the measuring range for both devices. In a method comparison, higher values of BHB were measured by KetoSure than by an enzymatic endpoint spectrophotometric reference method (mean bias: 21% (95% confidence interval (CI): 4%–37%)). Conversely, StatStrip Express returned lower values of BHB (mean bias: −16% (95% CI: −38%–7%), while the widely applied POCT device FreeStyle Precision Neo (Abbott) returned values equivalent with the reference method (mean bias: 5% (95% CI: −14%–24%). In samples with concentrations of BHB above the measuring range, the POCT devices could be provoked to return falsely low results. In conclusion, the quality of KetoSure is in line with other established POCT devices; however, the KetoSure measures higher concentrations than other POCT devices. As, linearity only was observed in the lower part of the measuring range and as falsely low measures could be provoked, we advise users to interpret results with precaution.

Pilot-scale study on the suspension of drill cuttings: Effect of fiber and fluid characteristics

Drilling operation smoothness is essential to minimize drilling non-productive time (NPT). Inadequate cuttings transport can result in cuttings bed build-up in the wellbore low side, jeopardizing the whole drilling operation. Modifying base fluid properties can prevent the associated risks with poor hole cleaning. Fiber sweeps enhanced hole-cleaning performance and increased cuttings transport to the surface with a negligible increase in fluid viscosity. This experimental investigation examines the effect of fibers on the cuttings carrying capacity of carboxyl methylcellulose (CMC) based polymeric fluids. The carrying capacity of the fluids is assessed by measuring the suspended and deposited cuttings/solids concentrations after a predefined quiescent period. Synthetic monofilament fiber of 0.9 specific gravity, 100 μm diameter, and two different lengths (3 and 12 mm) were used. Polymer concentrations were 0.747 and 1.1 wt %, while fiber concentration was varied from 0.00 to 0.08 wt%. The investigation was performed using a vertically configured annular test section (150 cm long) with a rotating inner shaft speed. Experimental results indicate that increasing fiber or/and polymer concentrations significantly improved cuttings carrying capacity of the fluids in suspending fine (1 mm) particles.In most cases, the carrying capacity of the fluids in suspending coarse cuttings (3 and 6 mm particles) was slightly improved with the addition of polymer and fiber. Although the rotation of the inner shaft substantially reduced the deposition of fine particles but did not prevent the settling of coarse cuttings. In addition, short fibers demonstrated better performance than long ones in improving the carrying capacity of the fluids. Utilizing long and short mixed fibers at a specific proportion improved the cuttings carrying capacity of the fluid better than unmixed fiber.

Lost circulation mitigation using modified enzyme induced calcite precipitation technique

Circulation loss is a very common and undesirable non-productive time (NPT) event that happens during the oil and gas well drilling operations. Many loss circulation materials (LCM) have been formulated and implemented in the process of gas and oil wells drilling to minimize or mitigate this problem. In-situ calcite precipitation in effective pores can restrict the movement of the water flow between the grains by forming bridges. In this study, different mixes of Enzyme-induced calcite precipitation (EICP) solution are studied and tested in the laboratory to mitigate lost circulation problem. EICP solution is primarily composed of urea, calcium chloride, magnesium chloride, Xanthan Gum, and urease enzyme. Different concentrations and compositions of reagents were tested. The properties of the created precipitates were examined through different techniques such as X-ray diffraction analysis (XRD), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). The combination that produced the highest amount of thermally stable calcite with a minimal amount of aragonite, along with the highest precipitation efficiency was further selected for lost circulation application. A solution containing 1M Urea, 0.9M CaCl 2 , 0.1M MgCl 2 , 1 g/L Xanthan Gum, and 3 g/L urease was considered as an optimum combination for an EICP process. Lost circulation experiments were carried out in core flooding experiments on a high permeable Indian Limestone sample at room and elevated temperature conditions with the optimum combination of EICP reagents. The permeability of the samples was measured before and after the treatment. Nuclear Magnetic Resonance (NMR) scan was also run before and after the treatment to determine the pore size redistribution. Results showed that the permeability of the samples was reduced by 99%, NMR results showed that the porosity of the samples was also reduced by 20%. • Investigation of the modified enzyme induced calcite precipitation as a lost circulation technique. • Investigation of incorporating magnesium in an EICP process to precipitate dolomite mineral. • Investigation of incorporating Xanthan Gum as a temperature stabilizer for an EICP reaction. • Coreflooding and NMR experiments with optimized EICP solution as a potential lost circulation material. • 99% reduction in permeability achieved with modified EICP process.

Wellbore Flow Model and Process Optimization for Gas-Lift Leakage Drilling for Shallow Shale Formations.

The shallow surface karst landform in the Nanchuan-China shale gas area, with developed caves and underground rivers, frequently lost circulation during the drilling operation. To solve the issue, first, according to the actual drilling engineering, this paper analyzes the geological factors and drilling and completion characteristics, optimizes the construction plan, and suggests a new technology for gas-lift leakage drilling based on double-wall drill pipes. Second, a distributed coupling improved Beggs–Brill gas–liquid–solid multiphase flow model is established. This model is used to complete the optimization design of the gas-lift leakage penetration construction scheme of the well sy20-2. Finally, the accuracy, process feasibility, and effect of the model are verified after the field application. The test results show that this method can establish a full drilling fluid circulation without plugging the leakage, control the leakage rate to within 0.5 m3/h, more than 90% reduction in the loss of circulation, and significantly shorten the nonproduction time limit with good application prospects.

A simulation-based statistical method for planning modular construction manufacturing

Modular construction is a promising alternative to conventional construction; offering improved productivity, quality, and safety. To realize these benefits, sequencing the module fabrication process in a manner that ensures efficient allocation of labor resources is essential. However, the varying sizes and design specifications of modules lead to high variation in process times at workstations, ineffective utilization of resources, and imbalanced production lines. To address these challenges, this paper proposes a simulation-based statistical method to plan the sequencing of module fabrication and the allocation of workers at workstations for such that productivity and control are improved. The method consists of four processes: (i) data collection to obtain historical and near real-time data; (ii) identification of significant impact factors affecting process times at workstations along the production line; (iii) development of a predictive model for forecasting process times at workstations using statistical analysis and probability distribution function; and (iv) planning the sequencing of module fabrication in a manner that ensures efficient labor allocation (i.e., crew size). The developed method is validated using data captured from a light gauge steel wall panel production line operated by a modular fabricator in Edmonton, Canada. The industrial partner produces both interior and exterior light gauge steel wall panels on a production line consisting of multiple workstations. First, five significant impact factors for each workstation among the design factors that highly influence the process times were identified in order to develop cycle time formula as a predictive model. The simulation model developed and implemented in conjunction with cycle time formula (CTF) in this case study was deemed to be a reliable predictive model (i.e. 89.39% accuracy), which can be used to improve productivity. The method is shown to be capable of assisting in decision-making by enabling production managers to better understand the effects of proposed changes to the production line prior to implementation. In this way, production managers can plan effectively and thereby reducing non-productive idle time.

Low Equivalent Circulating Density Organoclay-Free Invert Emulsion Drilling Fluids

AbstractHigh pressure and high temperature (HPHT) wells especially those with narrow pore/fracture pressure gradient margins present challenges in drilling. Maintaining optimum and low rheology for such wells becomes a challenge where a slight change in the bottom-hole pressure conditions can lead to nonproductive time. However, maintaining low viscosity profile for a drilling fluid can pose a dual challenge in terms of maintaining effective hole-cleaning and barite-sag resistance. This paper describes the formulation of 95pcf medium-density organoclay-free invert emulsion drilling fluids (OCIEF) with a low viscosity profile. The fluids gave lower plastic viscosity (PV), which ensured that the fluid presents low equivalent circulating density (ECD) contribution while drilling/circulating. These fluids were formulated with acid-soluble manganese tetroxide as weighting agent and a specially designed bridging-agent package. The fluids were hot rolled at 300 °F and their filtration and rheological properties were measured. The paper describes the static-aging, contamination, and high pressure/high temperature rheology measurements of the fluids at 300 °F. Particle plugging experiments were performed on the fluids to determine the invasion characteristics and the nondamaging nature of the fluids. These organoclay-free invert emulsion fluids (OCIEFs) were then field-trialed in different wells with good results. Field deployment of the 95pcf organoclay-free invert emulsion fluid helped to maintain the required hole stability in the high temperature and high pressure (HTHP) well. The well was displaced to 95pcf production screen test (PST) fluid and completed with a 4 ½ in. sand screen. The paper demonstrates the superior performance of the developed fluid in achieving the desired lab and field performance.

Restructuring picking and restocking processes on a hypermarket

Abstract The purpose of this paper is to identify challenges and opportunities for exploring Lean approaches in managing instore logistics processes of a hypermarket, wherein data collection made to quantify the state of the system, methods, and instruments will be applied to improve picking and restocking processes between the two main stock areas, the store and the warehouse. This article reports on a case study work, comprising direct observation, and analysis of the data collected, which was developed in three main phases: identification and characterization of problems, improvements in management of stock and restocking information, and finally improvements in the picking and restoking processes and teams. After analysing the hypermarket, some problems were found such as a high percentage of non-productive time in restocking, a daily average of 132 products to be restocked, a lack of standardization of tasks in the warehouse, and outdated restocking management system. Therefore, there will be some proposals to change the restocking management system, the picking and restocking work method and the warehouse layout, which makes it possible to decrease stock breaks in the store by 17%, the number of employees dedicated to these processes from 14 to 11, and finally, to reduce 2 daily hours of non-adding value time. This work makes an innovative contribution to fulfil a lack of publications dedicated to hyper-market inbound logistics between the warehouse and the store. Thus, it reveals the significant importance of focusing on instore logistics to the point of sale, concerning the efficiency, the lean production, and social benefits to the retail hypermarket, from the reception of products in the hypermarket warehouse until they are placed on the store shelves.

Drilling the Undrillable; a Review of Indonesia Onshore Managed Pressure Drilling (MPD) Operation Experiences

MPD (Managed Pressure Drilling) has been used worldwide to overcome drilling challenges in onshore and offshore operations. MPD has proven to save significant cost efficiency in every applications, by reducing NPT (Non Productive Time) caused by drilling problems such as, severe loss circulations, loss-kick cycle, and well control events. Indonesia is one of major country in Asia Pacific that has several needs to developing or exploring new opportunities with drilling challenges, especially in vuggular carbonate reservoirs, narrow drilling windows, and wellbore in-stability. This paper will present the basic theory on MPD, MPD equipment’s, types of MPD, and elaborate more on successful MPD operation in onshore Indonesia, and clustering the solutions of drilling challenges.

Wear-adaptive optimization of in-process conditioning parameters during face plunge grinding of PcBN

Polycrystalline cubic boron nitride is a very hard material. Machining of this material is performed by grinding with diamond tools. Due to its high hardness, grinding tools are subjected to severe microscopic and macroscopic tool wear. This wear leads to short tool life and results in high effort in conditioning the abrasive layer. Contrary to the usual conditioning of diamond grinding wheels with diamond dressing tools, this study investigates a conditioning process based entirely on the use of white corundum cup rolls. These conditioning tools allow the in-process face plunge conditioning of vitrified bond diamond grinding tools. The circumferential speed of the conditioning tool and the average grain diameter of the corundum are identified as the main factors influencing the topography of the generated grinding layer. To describe the performance of the conditioning process, a specific conditioning removal rate Q_{sd}^{prime } is derived. This parameter represents a cumulated variable that allows a comparison of different conditioning strategies. It is shown that an increase in Q_{sd}^{prime } significantly counteracts microscopic wear on the abrasive layer. Therefore, optimized process parameters enable the process of in-process conditioning to significantly reduce wear on the grinding tool without increasing the process time or the non-productive time.

Investigation of Mud Related Wellbore Problems in Drilling Well-A in the Niger Delta Region

Wellbore instabilities due to mud related issues during well construction in the wetland Niger Delta region were analyzed, reviewing well A as a case study. Time distribution plots and activity break down summary were evaluated for the candidate well. From the analyses, drill bit and bottom hole assembly balling, largely due to soft sticky and reactive gumbo shale with high swelling and dispersive tendencies in the mud system, severe down hole losses, hole pack off, pipe stuck potentials and kicks are some likely mud related challenges often encountered drilling in the Niger Delta region. Consequent upon which there would be reduced rate of penetration, tight spot, increased torque and drag, as seen in well-A. The drilling fluids play pivotal roles in oil extraction processes as their type; design and formulation determine the ultimate success of the entire drilling operation and overall production performance of the well. A poorly designed mud system may trigger wellbore instability that could lead to non-productive time, loss of drilling equipment, even loss of the entire well, kick and blowout scenarios due to loss of primary and secondary barriers. Formation damage may occur, which ultimately leads to reduction in permeability and poor well performance,

Lost circulation prediction based on machine learning

Lost circulation, one of the most headache problems in drilling engineering, causes a significant increase in non-productive time and increases the uncertainty of well control risk. There is a tremendous challenge for lost circulation prediction/diagnosis using traditional methods in carbonate formation due to its complex and variable leakage zone. To solve this problem of the Mishrif reservoir in the H oil field, in this paper, we propose a whole practical workflow of lost circulation prediction based on the Mixture Density Network using deep learning. Firstly, we choose 16 comprehensive mudlogging parameters that are the most correlated with the mud loss rate. These parameters are selected from a total of 22 comprehensive mudlogging parameters using three different feature selection approach. Then, combining the parameters and the mud loss rate, we get the relationship between comprehensive mudlogging parameters and the mud loss rate based on the Mixture Density Network including five sub-Gaussian models. Finally, the mud loss rate distribution is obtained according to the relationship with input parameters in real-time. The reliability is also evaluated using the uncertainty information in the model. The results show that the parameters had the highest correlation with the mud loss rate, including Measure Depth, Vertical Depth, Rate of Penetration, Hook load, Pump Pressure, Stroke Per Minute, Flow In, Flow Line, Temperature In, Temperature Out, Mud weight In, Mud weight Out, Conductivity Out, Equivalent Circulating Density, Total Gas and Pit Volume Total. The Mixture Density Network has a solid ability to describe data suitable for mud loss prediction and diagnosis. Improving the quality of training data is very important to improve the prediction accuracy of the model. Due to uncertainty in the weights of the sub-Gaussian model obtained from training, the final probability density distribution curve may be single or multi-peaked. According to the prediction results, lost circulation can be controlled by the corresponding method. Thus, the workflow not only allows for real-time assessment of lost circulation risk during drilling but also provides a reference for optimizing lost circulation control.

Experimental and numerical study of cuttings transport in inclined drilling operations

Directional drilling has become popular in recent decades in both onshore and offshore operations due to reduced drilling costs and improved recovery. In a directional well drilling operation, drill cuttings tend to settle down at the lower side of the inclined annular section. If the generated cuttings are not removed from the hole section properly, it causes a cuttings bed formation in the annular section. Different drilling related problem such as poor rate of penetration, excessive torque and drag, increase differential sticking often associated with a poor hole cleaning which eventually lead to increase in non-productive time (NPT). Therefore, the success of inclined well drilling operation largely depends on effective cleaning of drill cuttings from the annular section. A variety of parameters, including the fluid rheology, mud velocity, cuttings size, drill pipe rotation and drill pipe inclination generally influence the cuttings transport performance. Optimization of these drilling parameters is crucial to ensure proper hole cleaning. In this study, a Computational Fluid Dynamics (CFD) model for the inclined well section is used to investigate the cuttings transport efficiency (CTE). An Eulerian-Eulerian multiphase flow model is proposed and validated with lab scale experimental data. The experiments were performed in a 6.16-m-long annular test section having an outer pipe diameter of 4.5-inch and an inner pipe diameter of 2.5-inch. The setup is equipped with Electrical Resistance Tomography (ERT) system to measure the instantaneous solid volume fraction and a visualization section. A non-Newtonian Heschel-Bulkley (HB) fluid was used as drilling mud and solid glass beads of 2.50 mm–3.00 mm were used as cuttings in the experiment. This study shows a good agreement in visualization of mechanistic three-layer model of cuttings transport in terms of ERT data from experiment and CFD simulation. The validated CFD model is used to perform 5- Factors factorial design and analysis of variance (ANOVA) study. ANOVA shows that the interaction effects of mud velocity-cuttings size, mud velocity-inclination are statistically significant. Finally, this study proposed a statistical model to estimate the CTE of an inclined well considering the two factor and three factor interactions among the variables. Also, the model shows that drill pipe rotation has negligible effect in improving cuttings transport efficiency in the inclined well. The proposed model also reveals that cuttings size and fluid velocity account for 78% contribution in the transport efficiency for an inclined well. Furthermore, an Artificial Neural Network (ANN) method is used to verify the contribution of lower order interaction in the statistical model. Though empirical model shows few lower order two factor interactions (fluid rheology -cuttings size, fluid rheology-inclination), and three factor interactions (velocity-cuttings size-inclination, fluid rheology-cuttings size-inclination); ANN model shows that lower order interaction are significant in model prediction and should not be ignored. The findings of this study can help in better understanding the interaction behaviour among drilling parameters and optimized cuttings transport efficiency in the inclined drilling operation for a wide variety drilling parameters.

An integrated geomechanics approach to evaluate and manage wellbore stability in a deep graben formation in Tarim Basin

An ultra-deep exploration well has been drilled in the Tarim Basin, northwest of China, with an Ordovician target zone is at a depth of over 7500 m. The Ordovician formation is within a graben structure. Extensive bedding planes and natural fractures develop in the target formation according to seismic analysis and image logging data from several reference wells, indicating a high risk of borehole instability or other complications while drilling through the fragmentized Ordovician zone. To mitigate potential borehole instability problems and associated nonproductive time during drilling the well, an integrated geomechanical study is conducted that combines geological, seismic, logging, and drilling data. The pore pressure, collapse pressure, and fracture pressure of the well are estimated and a safe mud window is provided to guide drilling operations in the Ordovician graben formation. A fracture development index is also calculated to locate the fracture distribution zones. The factors affecting wellbore stability are analyzed by establishing a discrete element model. With the guidance of this study, the well was successfully drilled and the stability of the wellbore was well controlled during drilling in the fractured formation.