Non-productive Time Research Articles

Overview of Leak-Off Test and Formation Integrity Test: Test Interpretation and Pitfalls.

Formation strength tests, such as formation integrity test (FIT), leak-off test (LOT), and extended leak-off test (XLOT), are crucial for maintaining wellbore stability. Wellbore stability issues are the main reason for nonproductive time (NPT) especially in the drilling operation. Therefore, it is important to understand the stresses around the wellbore to better interpret the formation strength tests. These tests are carried out to (i) test the casing shoe strength, (ii) estimate the minimum horizontal stress, (iii) derive the maximum horizontal stress, and (iv) estimate the fracture pressure gradient. By understanding the stresses around the wellbore and the results of the formation strength tests, engineers can make informed decisions about casing design, cementing practices, and drilling operations. This can help to prevent wellbore instability and NPT, which can save time and money. This study comprehensively reviewed, examined, and analyzed all the available tests currently employed in the field. The goal is to provide an in-depth understanding of each technique and to identify any potential limitations. A deep literature review was conducted to identify all the available tests. Each test was examined in detail to understand its purpose, methodology, and limitations. Then, the results of the tests were analyzed to identify any trends or patterns. The findings of the study were summarized and presented in this paper. Several factors were considered in the analysis of the tests such as formation permeability, formation strength, stresses around the wellbore, fluid properties, and mud type. The results of the study can be used to improve the understanding of formation strength tests and to develop more accurate and reliable methods for interpreting the results. The study considers several factors in the analysis of the tests, such as formation permeability, formation strength, stresses around the wellbore, fluid properties, and mud type. This is different from previous studies, which have often focused on a single factor or a small number of factors.

Drilling dynamics measurement of drilling motors and its application in recognition of motor operation states through machine learning

Drilling motors are widely used in unconventional oil and gas exploration. Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors, predictive maintenance for drilling motors is necessary to optimize asset utilization. However, service companies face significant challenges in achieving predictive maintenance: operational data acquisition, automated statistics analysis, and drilling state recognition. This paper presents a miniature vibration recorder, an automatic statistical analysis method, and a layered recognition algorithm to resolve these challenges and improve tool maintenance efficiency. The designed recorder can be installed in the catch of a conventional mud motor to record drilling dynamics over a drilling motor's entire operation cycle. Time-series data from the recorder can be used to automatically generate operation statistics, mitigating the costs incurred by manual data analysis. The layered recognition algorithm then enables the automatic identification of drilling operation states, i.e., surface, downhole non-drilling, downhole sliding, and downhole rotation. The solutions were validated by deploying the recorder in drilling field runs and analyzing recorded data using the associated design software, yielding a functional data collection, automatic data statistical analysis, and operation state recognition accuracy of 95%. Through achieving improved data collection and analysis, the recorder and software introduced in this work can notify motor owners of the detailed operation history of their tools and enable informed preventive maintenance.

Improving Water-Based Drilling Mud Performance Using Biopolymer Gum: Integrating Experimental and Machine Learning Techniques.

Drilling through shale formations can be expensive and time-consuming due to the instability of the wellbore. Further, there is a need to develop inhibitors that are environmentally friendly. Our study discovered a cost-effective solution to this problem using Gum Arabic (ArG). We evaluated the inhibition potential of an ArG clay swelling inhibitor and fluid loss controller in water-based mud (WBM) by conducting a linear swelling test, capillary suction timer test, and zeta potential, fluid loss, and rheology tests. Our results displayed a significant reduction in linear swelling of bentonite clay (Na-Ben) by up to 36.1% at a concentration of 1.0 wt. % ArG. The capillary suction timer (CST) showed that capillary suction time also increased with the increase in the concentration of ArG, which indicates the fluid-loss-controlling potential of ArG. Adding ArG to the drilling mud prominently decreased fluid loss by up to 50%. Further, ArG reduced the shear stresses of the base mud, showing its inhibition and friction-reducing effect. These findings suggest that ArG is a strong candidate for an alternate green swelling inhibitor and fluid loss controller in WBM. Introducing this new green additive could significantly reduce non-productive time and costs associated with wellbore instability while drilling. Further, a dynamic linear swelling model, based on machine learning (ML), was created to forecast the linear swelling capacity of clay samples treated with ArG. The ML model proposed demonstrates exceptional accuracy (R2 score = 0.998 on testing) in predicting the swelling properties of ArG in drilling mud.

Evaluasi Invisible Lost Time dan Non-Productive Time Pada Sumur X Menggunakan Data End Of Well

One of the characteristics of the oil and gas sector, especially drilling activities, is High Cost, namely high operational costs. Therefore, efficient and effective use of time is necessary to reduce operational costs. Evaluation of the influence of ILT and NPT on drilling activities was carried out to find the factors that made drilling time so long. Identifying KPIs for each activity is necessary to find out what activities can be optimized. From the analysis results it was found that the Invisible Lost Time value was 33.12% (198.75 hours) and Non-Productive Time was 18.37% (110.25 hours) caused by less than optimal performance and equipment failure. The author believes that the factors causing the relatively high ILT and NPT can still be optimized so that the costs required during drilling activities are in accordance with the desired planning.

Time-dependent thermal degradation of lost circulation materials in geothermal systems

Treatment of lost circulation can represent anywhere from 5 to 25 % of the cost in drilling geothermal wells. The cost of the materials used for lost circulation treatment is less important than their effectiveness at reducing fluid losses. In geothermal systems, the high temperatures (>90 °C) are expected to degrade many commonly used lost circulation materials over time. This degradation could compromise different materials ability to mitigate fluid loss, creating more non-productive time as multiple treatments are needed, but may result in recovering desired permeability zones within the reservoir section over time. This research aimed to study how thermal degradation of eight different lost circulation materials affected their properties relevant to sealing loss zones in geothermal wells. Mass loss experiments were conducted with each material at temperatures of 90–250 °C for 1–42 days to measure the breakdown of the material at geothermal conditions, collecting gases during several experiments to determine the waste produced during degradation. Compaction experiments were conducted with the degraded materials to show how temperatures reduced the rigidity and increased packing of the materials. Viscosity tests were conducted to show the impact of different materials on drilling fluid rheology. Microscope observations were conducted to characterize the alterations to each material due to thermal degradation. Organic materials tend to degrade more than inorganic materials, with organics like microcellulose, cotton seed hulls and sawdust losing 30–50 % of their mass after 1 day of heating at 200 °C, while inorganics like magma fiber only lose ∼5–10 % of its mass after one day of heating at 200 °C. Granular materials are the strongest when compacted despite any mass loss, while fibrous and flaky materials are fairly weak and breakdown easily under stress. The materials do not generally affect fluid rheology unless they have a viscosifying agent as part of the mixture. Microscopic analysis showed that more rigid materials like microcellulose and cedar fiber degrade in brittle manners with splitting and fracturing, while others like cotton seed hulls degrade in more ductile manners forming meshes or clumps of material. The thermal breakdown of lost circulation materials tested suggests that each material should also be classified by its degree of thermal degradability, as at certain temperatures the materials can lose the capability to bridge loss zones around the wellbore.

1D Geomechanical Model and Wellbore Stability Analysis for Kafr El-Sheikh Formation at Sapphire Field, West Delta Deep Marine, Egypt

The Messinian Abu Madi Formation considered the most prospective reservoir target in the Nile Delta, is situated under the Pliocene Kafr El-Sheikh Formation, where most non-productive time takes place. Nevertheless, reaching the desired reservoir presents a greater level of complexity than first perceived. To mitigate the occurrence of significant non-productive time caused by wellbore instability problems, including stuck pipes, tight holes, pack-offs, and jarring or fishing operations, there has been an increased focus on geomechanical analysis. Therefore, this work aims to offer a comprehensive 1D mechanical earth model for a specific set of wells situated in the sapphire field, which is situated inside the West Delta Deep Marine region. The mechanical parameters of the rock were determined by the use of empirical equations, since the absence of core sample data necessitated reliance on wireline data. The analysis of the borehole image has provided clarification that the orientation of the maximum horizontal stress is aligned with the NNE-SSW direction. The calibration of this model was conducted using data obtained from wellbore stability events that occurred during the drilling operations of these wells. This model would be valuable in forecasting and minimizing prospective problems by predicating the proper safe mud window. Sapphire-Da, the primary key, is utilized in this particular piece of writing.

Estimation of Surge and Swab Pressures While Tripping for Minimizing Excessive Drilling Problems

Surge and swab pressures result from pipe movement upward/downward during drill string reciprocation and tripping. surge and swab create costly drilling concerns such well kick, formation breakage, and lost circulation. This study aims to find the optimum pipe tripping speed to avoid surge and swab issues, particularly in lost circulation zones. Also, to determine how drilling factors and well bore geometry affect surge and swab pressures. Mud rheology, mud properties, hole diameters, drill pipe diameter, and bottom hole assembly (BHA) dimensions were also analyzed. Dynamic surge and swab model was employed to simulate two case studies in Rumaila Iraqi oilfield. The findings indicated that the tripping speed is the most effective variable on surge and swab pressures. Annular clearance and BHA design have also a major effect on the surge and swab pressures. For the first case study, it is found that whenever the mud weight is far from the pore pressure and fracture pressure gradients, the 5" drill pipe can be tripped in/out of lost circulation formation (Shuaibba formation) at optimum tripping speed of (80 ft/min) without surge/ swab related problems. The reason is that the generated surge pressures (6110-6300 psi) are below the fracture pressure (7100-7400 psi). The results also showed that tripping 5" drill pipe within a maximum running speed of 80 ft/min in (8.5") open hole section through Tanuma formation till Rumaila formation causes a surge pressures of (4650-5050 psi) which is still below the fracture pressures of formations (4750-5660 psi). the results of the second case study (S-shaped well) revealed that the optimum tripping speed of the drill string in the (12.25") open hole through Dammam and Hartha formations is (90 ft/min). it is also concluded that pulling out of hole (POOH) process must be carried out more carefully than the running process since the mud density is near the pore pressure gradient. The results of this study can be further used to improve the drilling efficiency in Rumaila oil field with decreasing non-productive time (NPT) by employing the appropriate tripping speed and optimal drilling operations.

STUCK PIPE MITIGATION: FINDINGS, ANALYSIS, AND MACHINE LEARNING SOLUTIONS

The occurrence of stuck pipes during drilling presents significant challenges, leading to nonproductive time (NPT) and potential damage to equipment. This paper delves into the reasons behind, effects of, and methods for addressing incidents of stuck pipes in drilling actions. Extensive literature review and analysis reveal the prevalence of stuck pipe events and their diverse underlying causes, ranging from differential sticking to mechanical jamming. Additionally, the study identifies key drilling parameters and signals indicative of potential stuck pipe incidents, emphasizing the importance of early detection and intervention to prevent adverse outcomes. Proposing the utilization of machine learning algorithms and statistical models for predictive analytics as a proactive strategy to mitigate the risk of stuck pipe incidents. The paper discusses the limitations of traditional statistical models and highlights the efficacy of machine learning methods, such as artificial neural networks (ANNs) and support vector machines (SVMs), in accurately modeling drilling processes. Furthermore, practical machine learning models using real-time drilling data are presented as effective tools for early detection and timely intervention in stuck pipe incidents. The study also describes various software tools used to assess and mitigate risks in drilling operations. Overall, this paper presents prospects for improving operational efficiency and safety of drilling operations using advanced analytical methods and software solutions. Keywords: stuck pipe, type of stuck, drillstring immobilization, nonproductive time, machine learning in drilling, risk analysis software.

Auditing and Analysis of Natural Gas Consumptions in Small- and Medium-Sized Industrial Facilities in the Greater Toronto Area for Energy Conservation Opportunities

This paper presents the findings of fifteen energy audits conducted on industrial sites in Canada’s Greater Toronto Area (GTA). The audits covered a range of industries including food processing, packaged goods, and finishing processes (powder-coating). The primary focus of the audits was to analyze the natural gas consumption and the performance of major-gas-consuming equipment. The audits identified natural-gas-consuming equipment that could be optimized to yield energy and operational cost savings and greenhouse gas (GHG) reduction opportunities. Food production plants’ energy intensity ranges from 5.59 m3/ft2 to 17.73 m3/ft2. Therefore, there is a significant opportunity to improve energy consumption through better technology integration. The results of the audits indicate a trend of an increase in the percentage of non-productive consumption with non-productive time. The proposed energy-saving measures include reducing non-productive natural gas consumption, gas-fired equipment tune-up, optimizing boiler loads, and reducing oven exhaust by using variable frequency drives (VFDs). The findings of this study could be used to develop a demand-side management program specifically for small- and medium-sized industrial facilities in the Greater Toronto Area and other parts of Canada.

Field Validation of a Universally Applicable Condition-Based Maintenance System for Mud Pumps

Summary Although mud pumps are considered to be critical rig equipment, their health monitoring currently still relies on infrequent human observation and monitoring. This approach often fails to detect pump damage at an early stage, resulting in nonproductive time (NPT) and increased well construction costs when initial damage progresses and pumps go down unexpectedly and catastrophically. Automated approaches to condition-based maintenance (CBM) of mud pumps to date have failed due to the lack of a generalized solution applicable to any pump type and/or operating conditions. This paper presents a field-validated universally applicable solution to mud pump CBM. The system uses a sensor package that includes acoustic emission sensors and accelerometers in combination with anomaly detection deep learning data analysis to pinpoint any abnormal behavior of the pump and its components. The deep learning models are trained with undamaged normal state data only, and a damage score characterizing the extent of damage to the mud pump is calculated to identify the earliest signs of damage. The system can then generate alerts to notify the rig crew of the damage level of key mud pump components, prompting proactive maintenance actions. Field tests were conducted while drilling an unconventional shale well in west Texas, USA, and a geothermal well in Japan (i.e., two very different drilling operations) to verify the feasibility and general applicability of the developed pump CBM solution. Sensors were attached to pump modules, and data were collected and analyzed using the deep learning models during drilling operations. During the field tests, different hyperparameters and features were compared to select the most effective ones for identifying damage while at the same time delivering low false positive rates (i.e., false alarms during normal state pump operation). The system required only several hours of normal state data for training with no prior pump information. Moreover, it correctly identified the degradation of the pump, swabs, and valves and produced early alerts several hours (in the range of 0.5–17 hours) before actual pump maintenance action was taken by the rig crew. This generally applicable pump CBM system eliminates the environmental, health, and safety concerns that can occur during human-based observations of mud pump health and avoids unnecessary NPT associated with catastrophic pump failures. The final version of this system will be a fully self-contained magnetically attachable box containing sensors and a processor, generating simple indicators for recommending proactive pump maintenance tasks when needed.

Optimization of drilling design at different well depths with respect to specific energy

Drilling operations cost millions of Dollars and its success depend largely on some key parameters. Some of these parameters include wait on bit (WOB), rate of penetration (ROP), revolution per minute (RPM) and specific energy, also known as specific mechanic energy. In the design of the drilling program, keen interest must be paid to their variation, else, drilling problems which will lead to non-productive time will ensue. Therefore, this study focused on the optimization of bit design through selection of the bit with lowest specific energy possible to reduce drilling cost and improve efficiency via the consideration of critical parameters such as WOB, ROP drilling cost per foot and specific energy (Mechanical specific energy). In this study, data were collected from 5 wells – ASSA North 004, ASSA North 005, ASSA North 006, Gbetiokun 7 and Benisede, all in Niger Delta. Specific energy was computed using mechanic energy and specific energy relations while the cost optimization was done using drilling cost relation. These data were carefully analyzed and the results of specific energy for the five wells selected wells were obtained. The values of the specific energy at desired depths were used to track the performance of drill bits at that depth. The bit used for ASSA North 005 was found to be most suitable as it maintained lowest specific energy for different depths. The study therefore gave an insight into the importance of specific energy of the drill bit while carry out bit selection for an efficient and cost-effective drilling operation. The bit used for the drilling of ASSA North well was selected because it has the minimum specific energy.

Hole cleaning evaluation and installation spacing optimization of cuttings bed remover in extended-reach drilling

In extended-reach or long-horizontal drilling, cuttings usually deposit at the bottom of the annulus. Once cuttings accumulate to a certain thickness, complex problems such as excessive torque and drag, tubing buckling, and pipe stuck probably occur, which results in a lot of non-productive time and remedial operations. Cuttings bed remover can efficiently destroy deposited cuttings in time through hydraulic and mechanical stirring effects. This paper aims to build a method for hole cleaning evaluation and installation spacing optimization of cuttings bed remover to improve the wellbore cleaning effect. Firstly, a Computational Fluid Dynamics approach with Eulerian–Eulerian multiphase model was utilized to investigate the mechanism of cuttings transportation, and a new type of cuttings bed remover was designed. Next, an evaluation method of hole cleaning effect of remover was established. After that, the effects of several drilling parameters on hole cleaning including flow rate of drilling fluid, rotational speed of drillpipe, rate of penetration, wellbore size, rheological property of drilling fluid, and remover eccentricity on the performance of cuttings bed remover were investigated. The results demonstrate that the new type of remover with streamline blade performs better than conventional removers. The efficiency of hole cleaning is greatly improved by increasing the rotational speed of drillpipe, flow rate of drilling fluid, remover eccentricity, and 6 rpm Fann dial reading for drilling fluid. While higher rate of penetration and large wellbore size result in worse hole cleaning. These findings can serve as an important guide for the structure optimization design of cuttings bed remover and installation spacing of removers.

Building 1D Mechanical Earth Model for Subba Oilfield in Iraq

At the Subba oil field, wellbore stability is the main concern while drilling. The wellbore's instability causes several issues, including: (inefficient hole cleaning, tight hole, stuck pipe, mud losses, caving, bad cementing, and well kick or blowout). This increases Non-Productive Time (NPT) and well-drilling costs; hence the operator's main goal is to create a drilling program that reduces these problems and therefore reduce drilling cost. The study aims to build a 1D mechanical earth model to predict the wellbore failure and design optimum mud weight to improve the drilling efficiency for future wells. The model includes pore pressure, stress state, and rock mechanical parameters (such as UCS, angle of friction, Young-Modulus, and Poisson-Ratio). To achieve this aim, the study utilised offset well data including log data (Gama-Ray Logs (GR), Caliper Logs (CALI), Density Logs (RHOZ), and Compressional Sonic (DTCO) and Shear Sonic (DTSM)), Core tests, Mini-frac field tests, Drilling Reports, Mud Reports, and Mud Log Reports (master log) to estimate and calibrate the profiles of formation pore pressure, rock mechanical properties, and in-situ stresses. The 1D mechanical earth model was built using the Excel program for three wells data set, where all the necessary parameters to create the model was calculated, calibrated for the calculated variables with core data and pressure test points, and finally the safe mud window was detected. The results showed that the Eaton Slowness method to predict pore pressure perfectly matches the pressure test points. The most common fault regimes in the Subba oilfield are normal and strike-slip faults. The Modified Lade criteria showed a compatible match with drilling events and calliper log in predicting the failure zones, so it is the best criterion in determining minimum and maximum mud weight. Based on the results of this study and in comparison with the mud window used in drilling operations in the field, it is necessary to change the mud window used in drilling and adopt the safe MWW of this study in drilling new wells in this field and the area adjacent to the field.

Plug Your Way Through Downhole Restrictions

_ Unable to successfully deploy plug-and-perf bottomhole assemblies (BHA) to the desired depth has resulted in significant nonproductive time (NPT) in unconventional completions. These instances of NPT are primarily attributed to casing deformation, rather than plug presets. To address these downhole restrictions, frac plugs with smaller outer diameters (OD) were introduced for stage isolation. This reduction in plug OD minimizes the risk of getting stuck in tight spaces, but it also compromises pressure ratings. This raises the risk of skidding (slip losses, anchoring) and leads to increased fluid usage for pumpdown. These challenges not only risk well productivity, but also escalate completion time and costs. For a comprehensive solution, a smaller-OD frac plug is necessary to pass through tight spots, while maintaining anchoring, sealing, and reducing required pumped-fluid volume. The Gap The annular clearance between the plug OD and casing inner diameter (ID) significantly influences plug-and-perf operations. A smaller gap leads to advantages such as lower expansion rates for isolation, better anchoring, and reduced pumped-fluid volume, but it also heightens the risk of obstructions. Conversely, a larger gap requires higher expansion rates for isolation, resulting in weaker anchoring, lower pressure rating, and increased fluid consumption, but reduces the likelihood of obstructions. In some frac operations, higher pressure is needed to break down the formation; thus, frac plugs with higher pressure ratings (smaller gap) are commonly favored. For instance, in 5.5-in., 20-lb/ft casing, typical frac plugs have an average OD of 4.4 in., an expansion rate of 8–12%, and a pressure rating of 10,000 psi. In many 5.5-in., 20 lb/ft casedhole completions across the US and parts of China, the annular clearance between casing ID and frac plug OD is approximately 0.2 in. on the x-axis and 0.4 in. on the y-axis (Fig. 1). In this confined space, encountering debris, sand accumulation, or a deformed section during pumpdown can obstruct or fully trap the plug. To mitigate these uncertainties, smaller-OD plugs are used for such “emergency” scenarios. Smaller-OD plugs have improved chances of navigating through tight spots, but they necessitate higher expansion rates for isolation, resulting in lower pressure ratings. For example, in 5.5-in., 20 lb/ft casing, reduced OD plugs have an average OD of 3.8 in., an expansion rate of 20–25%, and a pressure rating of 5,000 psi. They are more likely to fail due to their pressure limitations and use more fluid for pumpdown. In cases where the use of normal-OD plugs is not feasible, opting for smaller-OD plugs, even though they might have a higher risk of failure, can be a valuable last resort. This approach is sometimes favored over entirely skipping the problematic stages without any form of stimulation.

Numerical simulation study on impact factors to dynamic filtration loss

During drilling operations, naturally, fractured formations are prone to show serious mud losses, which hinder drilling and increase nonproductive time and costs. The influencing factors of dynamic fluid loss are important for optimizing drilling parameters, reducing drilling fluid loss, and protecting oil and gas reservoirs. In this study, we simulated the dynamic filtration loss of drilling fluid during drilling under formation conditions using commercial software CMG (Computer Modelling Group). The effects of filtration time, filtrate viscosity, pressure difference, internal filter cake permeability, and external filter cake permeability on filtration loss were investigated. The simulation results showed that the permeability of the external mud cake is an important factor to control the fluid loss, and the pressure consumed by the external mud cake with low permeability can account for more than 90% of the total pressure difference. When the permeability of the external mud cake is high, the permeability of the internal mud cake also has a significant influence on the dynamic fluid loss. Under formation conditions, the dynamic fluid loss of radial fluid loss is still proportional to the filtration time and pressure difference, and inversely proportional to the filtrate viscosity of drilling fluid. Under the simulated conditions, the pressure will quickly transfer to the boundary, and the formation pressure at the same position in the formation will gradually increase, while the increase is relatively small with a constant filtration rate. The results of this paper can be used to the real site for drilling optimization. This numerical analysis method can be easily applied to filtrate loss analysis, formation damage area calculation, and other radial flow-related study.

Interpretable Lost Circulation Analysis: Labeled, Identified, and Analyzed Lost Circulation in Drilling Operations

Summary Lost circulation (LC) is a serious problem in drilling operations, as it increases nonproductive time and costs. It can occur due to various complex factors, such as geological parameters, drilling fluid properties, and operational drilling parameters, either individually or in combination. Therefore, studying the types, influencing factors, and causes of LC is crucial for effectively improving prevention and plugging techniques. Currently, the expert diagnosis of LC types relies heavily on the experience and judgment of experts, which may lead to inconsistencies and biases. Additionally, difficulties in obtaining data or missing important data can affect the efficiency and timeliness of diagnosis. Traditional physical modeling methods struggle to analyze complex factor correlations, and conventional machine learning techniques have limited interpretability. In this paper, we propose an interpretable lost circulation analysis (ILCA) framework that provides a new method for analyzing LC. First, we use Gaussian mixture model (GMM) clustering to analyze the LC characteristics of regional case data, efficiently and accurately labeling 296 LC events. Second, we establish the relationship between geological features, drilling fluid properties, operational drilling parameters, and LC types using the XGBoost algorithm. This enables timely identification of LC types during drilling operations using real-time data, with a precision greater than 85%. Finally, we use interpretable machine learning techniques to conduct a comprehensive quantitative analysis of influencing factors based on the established XGBoost model, providing a clear explanation for the identification model. This enables drilling engineers to gain deeper insights into the factors influencing LC events. In summary, the proposed ILCA framework is capable of efficiently labeling LC types based on regional case data, identifying LC types in a timely manner using real-time data, and conducting quantitative analysis of the factors and causes of LC. This approach addresses the limitations of traditional methods and offers valuable insights for drilling engineers.

Real-time automatic identification methods for downhole whirling based on mechanical specific energy model of drill bit

In the drilling exploitation of hot dry rock for geothermal energy, whirling is one of the main low-efficiency conditions that affect the efficiency of Polycrystalline Diamond Compact (PDC) bit in horizontal well drilling. Realizing automatic and real-time identification with whirling is of great significance to save non-productive time and ensure drilling safety and benefit. In this paper, we first constructed a real-time drilling mechanical specific energy (MSE) model combined with while-drilling testing to reflect the real-time drilling conditions. The MSE model is used to normalize multi-source parameters. Secondly, we constructed a Back Propagation (BP) artificial neural network (ANN), and then the normalization effect verification, optimization of network parameters, and identification effect verification of whirling were carried out. The final results show that the established MSE model has a favourable effect on data normalization, which could also reduce the complexity of the required network model, and shorten the training time by 20–30 s/step. The optimal algorithm is Trainscg, whose optimal number of hidden layer nodes is 5, and the optimal maximum number of iteration steps is 1000. The established ANN model can accurately identify whirling based on MSE, the accuracy is about 0.94, and the average relative error is 1.3%. The method established in this paper provides a reference for the automatic identification of various low-efficiency conditions based on MSE.

Interpretation of Mud Losses in Carbonates Based on Cuttings Description, Well-Logging, Seismic and Coherency Data

Hartha Formation is an overburdened horizon in the X-oilfield which generates a lot of Non-Productive Time (NPT) associated with drilling mud losses. This study has been conducted to investigate the loss events in this formation as well as to provide geological interpretations based on datasets from nine wells in this field of interest. The interpretation was based on different analyses including wireline logs, cuttings descriptions, image logs, and analog data. Seismic and coherency data were also used to formulate the geological interpretations and calibrate that with the loss events of the Hartha Fm. The results revealed that the upper part of the Hartha Fm. was identified as an interval capable of creating potential mud losses, resulting in high NPT. This is due to its diagenetic features such as succrosic dolomites and vuggy zones that could act as thief zones. Seismic potential was used for the prediction of the geological related non-productive drilling time in the Hartha interval. The seismic data quality in this interval was good, with geological observations already made. Detailed interpretation and analysis of the Hartha interval were performed and integrated with the existing seismic interpretation, rock properties, and NPT database to calibrate wells with the loss events to the seismic observations.

Study of Rheological Characteristics of a Water-Based Drilling Fluid in Presence of Biopolymers, Synthetic Polymer, and Modified Natural Polymer

The major contemplation in the recent decades is to minimize the operational difficulties during any drilling activity by improving the characteristics of water-based drilling fluids (WBDF). Such problem as a poor hole-cleaning because of settling of drill cutting and debris are extremely common. This problem will eventually convert into a serious non-productive time event in the form of pipe sticking. The most common additives added in the mud system to improve its rheological properties and tackle sedimentation issues are polymeric materials. During this study, four different polymers including sodium alginate and pure-bore as biopolymers, PHPA as a synthetic polymer, and the L-grade polyanionic cellulose (PAC-L) as a modified natural polymer have been used to improve rheological characteristics of WBDF. Laboratory measurements have indicated no considerable changes in the mud weight in the case of addition of these polymers. Moreover, these results reveal that all the systems behave as shear thinning fluid (flow index 1). A decrease in the dynamic viscosity with an increase in the shear rate has been observed for all samples. The disentangling of polymeric chains occurring at high shear rate eventually reduces the viscosity of these systems. In addition, samples that comprised of pure-bore and sodium alginate exhibit the desired operating magnitude of rheology (PV, YP, GS, AV, and YP/PV). These results show such particles minimize operational problems arising because of a poor hole-cleaning. In contrast, a system with PHPA and PAC-L has shown detrimental rheological characteristics. The high molecular weight of PHPA and the hydration of PAC-L in the presence of potassium chloride decrease their performance.

Predicting The Azimuth of Stress from Wellbore Breakouts in (X) Oil Field

The current minimum and maximum horizontal stresses orientations are influential parameters in the development of any Geomechanical model; these models aim to reduce wellbore instability and non-productive time during drilling by selecting the best wellbore direction when drilling inclined or horizontal wells. Minimum horizontal stress orientations can be established by examining wellbore breakouts. Borehole breakouts result from a natural failure compression process in which the maximum hoop stress surrounding the hole exceeds the rock strength, which may be detected using an oriented caliper log tool. There is no data in the World Stress Map (WSM) in the research area. As a result, data from 10 wells in the (X) oilfield's Oriented caliper well logs were analyzed for this study. The breakouts azimuth is consistent with depth, and the minimum horizontal stress azimuth from the north is 140o - 150o and 320o - 330o, which is compatible with the regional maximum horizontal stress direction (Shmax). As a result, the recommended drilling direction of deviated, and horizontal wells in the (X) oil field area should parallel the maximum horizontal stress.