Summary A survey program is designed for every well drilled to meet the well objective of penetrating the target reservoir and avoiding a collision with nearby offset wells. The selection of the wellbore survey tools within the survey program is limited in number and accuracy by the current surveying technologies available in the industry. This article demonstrates how a higher level of accuracy can be achieved to meet challenging well objectives when the accuracy of the most accurate wellbore surveying tools and technologies taken individually is insufficient. This high level of wellbore positioning accuracy is achieved by combining two independent wellbore positions of the same wellbore trajectory. The first wellbore position is calculated using the latest technology of magnetic measurement-while-drilling (MWD) definitive dynamic surveys (DDS). The accuracy of the MWD DDS can be further improved by minimizing error sources such as misalignment of the survey package from the borehole, drillstring magnetic interference, the use of localized geomagnetic reference, using high-accuracy accelerometer sensors, and a high-accuracy gravity reference. Furthermore, the MWD DDS inclination accuracy is improved using an independent inclination measurement from the rotary steerable system. A first wellbore position is calculated from the magnetic MWD DDS after applying in-field referencing (IFR), multistation analysis (MSA), bottomhole assembly (BHA), sag correction (SAG), and dual-inclination (DI) corrections to improve both azimuth and inclination accuracy. A second wellbore position is calculated using gyro-MWD (GWD) technology. The results and comparisons of multiple combined survey runs are presented. The highest accuracy of wellbore positioning had been proved in this successful case study by penetrating a very small reservoir target on an extended-reach well that was unfeasible using either the most accurate enhanced MWD DDS or GWD technology individually. The presented case study shows how the wellbore objectives of penetrating a very small reservoir target had been confirmed by logging-while-drilling images and the reservoir mapping interpretation of the client subsurface team. This gave a high-accuracy wellbore position during drilling and provided higher confidence in wellbore placement to maximize reservoir production without colliding with nearby offset wells. Wellbore survey accuracy limits a borehole’s lateral and true vertical depth (TVD) spacing, constraining reservoir production in those sections. In the top and intermediate sections, wellbore survey accuracy limits how close the wellbore can be drilled to other offset wells due to collision concerns. This directly impacts the complexity of the directional work and the cost per section. Combining independent wellbore surveys unlocks the potential to improve the wellbore positioning accuracy significantly. It demonstrates the highest wellbore positioning accuracy that can be achieved to date compared with the latest magnetic MWD surveys after correcting all known errors or compared with GWD.

Summary The success of an oilwell drilling operation is directly associated with the correct formulation of drilling fluids and their rheological measurements. The goal of this study is to investigate the usage of a Fann 35A viscometer and the methodology for rheological characterization of drilling fluids by comparison with the use of a rotational rheometer. Flow curves and gel strength tests were performed considering classic measurement artifacts such as apparent wall slip, secondary flows, steady-state (SS) regime, and inertial effects, among others. In addition, a study of the relationship between pressure drop and flow rate in a tube and in an annular space was carried out to investigate the influence of the viscosity function and of the rheological properties on the design of pipelines and the correct sizing of pumps. Use of American Petroleum Institute (API) equations and curve fitting were explored as potential choices for viscosity functions. The results indicate that the use of API equation predictions can compromise the effectiveness of the drilling process, while the choice of an adequate viscosity function is essential for the correct sizing of pumps. The gel strength was evaluated in the viscometer and presented divergent results from those obtained in the rheometer. Furthermore, a grooved geometry was developed for the viscometer to avoid the effects of apparent slip at low shear rates. Some recommendations are made based on the results obtained, which lead to better accuracy in the rheological results of drilling fluids and, consequently, better performance of some functions assigned to it. The proposed improvements and methodologies proved to be promising, although in some cases the cost-benefit remained unchanged.

Summary Machine learning (ML) has become a robust method for modeling field operations based on measurements. For example, wellbore cleanout is a critical operation that needs to be optimized to enhance the removal of solids to reduce problems associated with poor hole cleaning. However, as wellbore geometry becomes more complicated, predicting the cleaning performance of fluids becomes more challenging. As a result, optimization is often difficult. Therefore, this research focuses on developing a data-driven model for predicting hole cleaning in deviated wells to optimize drilling performance. More than 500 flow loop measurements from eight studies are used to formulate a suitable ML model to forecast hole cleanout in directional wells. Measurements were obtained from hole-cleaning experiments that were conducted using different loop configurations. Experiments ranged in test-section length from 22 to 100 ft, in hole diameter from 4 to 8 in., and in pipe diameter from 2 to 4.5 in. The experiments provided measured equilibrium bed height at a specific flow rate for various fluids, including water-based and synthetic-based fluids and fluids containing fibers. Several relevant test parameters, including fluid and cutting properties, well inclination, and drillstring rotation speed (drillpipe rev/min), were also considered in the analysis. The collected data have been analyzed using the Cross-Industry Standard Process for Data Mining. This paper is unique because it systematically evaluates various ML models for their ability to describe hole cleanout processes. Six different ML techniques: boosted decision tree (BDT), random forest (RF), linear regression, multivariate adaptive regression spline (MARS), neural networks, and support vector machine (SVM) have been evaluated to select the most appropriate method for predicting bed thickness in a wellbore. Also, we compared the predictions of the selected ML method with those of a mechanistic model for cases without drillstring rotation. Finally, using the ML model, a parametric study has been conducted to examine the impact of various parameters on the cleanout performance of selected fluids. The results show the relative influence of different variables on the prediction of cuttings bed. Accordingly, flow rate, drillpipe rev/min, and fluid behavior index have a strong impact on dimensionless bed thickness, while other parameters such as fluid consistency index, solids density and diameter, fiber concentration, and well inclination angle have a moderate effect. The BDT algorithm has provided the most accurate prediction with an R2 of 92%, a root-mean-square error (RMSE) of 0.06, and a mean absolute error (MAE) of roughly 0.05. A comparison between a mechanistic model and the selected ML technique shows that the ML model provided better predictions.

Summary The barrier material is a crucial component for wells, as it provides mechanical support to the casing and prevents the uncontrolled flow of formation fluids, ensuring zonal isolation. One of the essential prerequisites for the success of cementing an oil and gas well is the efficient removal of in-situ fluids and their adequate replacement by the barrier material. The quality of the mud displacement is affected by both the density and the viscosity hierarchy among subsequent fluids. Consequently, accurate and reliable measurement of fluid properties can help ensure consistent large-scale mixing of cementing fluids and verification that the properties of the mixed fluid are according to plan. In this paper, we investigate the implementation of a pipe viscometer for future automated measurements of density and viscosity of materials for zonal isolation and perform a sequential validation of the viscometer that starts with small-scale batch mixing and characterization of particle-free calibration liquids, followed by conventional Class G cement and selected new barrier materials. Finally, a larger-scale validation of the pipe viscometer was performed by integrating it into a yard-scale batch mixer for in-line characterization of expanding Class G oilwell cement mixing. In all cases, flow curves derived from pipe viscosity measurements were compared with offline measurements using a rheometer and a conventional oilfield viscometer. After a series of measurements and comparisons, the investigated in-line measurement system proved adequate for viscosity estimation. The flow curve of the barrier materials showed results similar to measurements using a conventional viscometer, validating the proposed test configuration to continuously measure the rheological behavior of the barrier material. The pipe viscometer flow curves are generally found to be in good quantitative agreement with independent viscometer characterization of the fluids, although some of the pipe viscometer measurements likely exhibited entrance length effects. Future improvements to the pipe viscometer design involve the assessment of even longer pipe sections to allow full flow development at the highest shear rate range and possibly different pipe diameters to improve the measurement resolution of low-shear rate viscosity.

Summary Warning signs of a possible kick during drilling operations can either be primary (flow rate increase and pit gain) or secondary (drilling break and pump pressure decrease). Drillers rely on pressure data at the surface to determine in-situ downhole conditions while drilling. The surface pressure reading is always available and accessible. However, understanding or interpretation of this data is often ambiguous. This study analyzes significant kick symptoms in the wellbore annulus both under static (shut in) and dynamic (drilling/circulating) conditions. We used both supervised and unsupervised learning techniques for flow regime identification and kick prognosis. These include an artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), decision trees, K-means clustering, and agglomerative clustering. We trained these machine learning models to detect kick symptoms from the gas evolution data collected between the point of kick initiation and the wellhead. All the machine learning techniques used in this work made excellent predictions with accuracy greater than or equal to 90%. For the supervised learning, the decision tree gave the overall best results, with an accuracy of 96% for air influx cases and 98% for carbon dioxide influx cases in both static and dynamic scenarios. For unsupervised learning, K-means clustering was the best, with Silhouette scores ranging from about 0.4 to 0.8. The mass rate per hydraulic diameter and the mixture viscosity yielded the best types of clusters. This is because they account for the fluid properties, flow rate, and flow geometry. Although computationally demanding, the machine learning models can use the surface/downhole pressure data to relay annular flow patterns while drilling. There have been several recent advances in drilling automation. However, this is still limited to gas kick identification and handling. This work provides an alternative and easily accessible primary kick detection tool for drillers based on data at the surface. It also relates this surface data to certain annular flow regime patterns to better tell the downhole story while drilling.

Summary Sealing elements (SEs) of fracture plugs have crucial roles to isolate target zones of a well in hydraulic fracturing. If the zonal isolation by the SE is not adequate, it can result in erosion of the casing. To the best of the authors’ knowledge, the effect of casing deformation on sealing performance is not well researched or understood. To study the effect of casing deformation on sealing performance, finite element analysis (FEA) of SEs in oval casings was conducted in this study. Finite element simulation of a degradable fracture plug with three different casings ovalities (0%, 2%, and 5%) and three different SE designs (O-ring type, short type, and traditional long type) was conducted to evaluate deformation behavior and sealing performance of SEs in deformed casings. Contact pressure (CPRESS) on the casing by the SE after the plug was set in the casing and the risk of leakage were discussed and compared for each design. In the casing with 0% ovality, all the SE designs established contact with the inner surface of the casing when setting force was applied. However, for the O-ring-type design, the area in contact with the casing was small and it may result in leak and erosion in the actual well if there is a small dent or deformation on the casing. When there is ovality in the casing, the minor inside diameter (ID) has a smaller ID and the major ID has a larger ID compared to the nominal ID of the casing. In the casing with 2% and 5% ovality, neither O-ring-type SE (O-SE) nor short-type SE (S-SE) could contact the major ID of the casing and there was a gap between the inner surface of the casing and the SE. This gap can cause erosion of the fracture plug and casing when the fluid passes through the gap. In contrast, the traditional long-type SE (L-SE) contacted both major and minor IDs of the casing, and no gap was observed. This result indicates that there is a potential risk of insufficient isolation of target zones and erosion of casings in actual well conditions if fracture plugs with S-SEs are used. Because there are various types of fracture plugs with different SE designs, this study helps to select proper fracture plugs with good SE design and mitigate the risk of erosion of casings and plugs. As this study is based on FEA simulations, future demonstrations through experiments and field trials are needed.

Summary The hydraulic effects on torque and drag modeling have been thoroughly studied in the past, yet their interpretation still causes a lot of misunderstandings and confusion. Historical models disregard the circulation effects and focus on the fluid mass by employing buoyancy forces based on the Archimedes principle. On the other hand, the reference model including the fluid circulation effects, introduced by R. F. Mitchell in the 1990s, consists in computing the forces due to internal and external fluids along the drillstring. The first type of model (called the Archimedes method) directly produces an effective tension, while the second one (generally called the pressure area method) produces a true tension that must be further transformed to obtain the effective tension. These different forms of tensions add even more confusion. By returning to the basic equations of the fluid effects on the drillstring, an equivalency between Archimedes and pressure area models has been found for the case with no circulation. Furthermore, with the same principle, an Archimedes-like model is deduced for the case of fluid circulation, where the effects of fluid pressures, frictions, and flows could be more easily interpreted. These two hydraulic models, after implementation in a true stiff-string 3D model, enable them to fairly compare the two approaches in terms of forces applied on the structure. The comparison of this Archimedes formulation with pressure area model gave sensibly the same results for various scenarios, proving the equivalency of the two approaches even with the case of circulating fluid. In addition to the model-to-model comparisons, torque and drag results are compared to field experiments at different depths. The flow rate was varied while reciprocating the drillstring up and down, and the hookload was recorded for each flow rate and each tripping direction. The model-to-data comparisons showed a good agreement between the theoretical results and experimental data. An advanced Archimedes method with all fluid circulation effects has been developed. By tackling the problem of circulating fluid in the drillstring using two different approaches and proving their equivalency, a better understanding of the hydraulic effects can be achieved, which in terms can help settle the possible debates and confusion that might arise by drilling engineers.

Summary Artificial intelligence (AI) is revolutionizing several businesses across the world, and its implementation in drilling engineering has enhanced the performance of oil and gas companies. This paper reviews and analyzes the successful application of AI techniques to predict wellbore instabilities during drilling operations. First, a summary of the implementation of AI for the prediction of loss circulation, pipe stuck, and mud window is highlighted. Then, the recent innovations and challenges of the AI adoption in major drilling companies is presented. Finally, recommendations are provided to improve the integration of AI in the drilling industry. This analysis gives deep insight into the main publications and recent advances of the application of AI in drilling engineering and is expected to contribute to the further development of the drilling industry.

Summary Due to volatile and uncertain oil and gas prices, the need to reduce well planning and drilling cost is a crucial concern in the oil and gas exploration and production industry. Therefore, the focus has increased on brownfield sites for new oil and gas explorations. However, the presence of previously drilled wellbores, also known as legacy wells, in brownfield sites increases the risk of collision incidents during the drilling process. Thus, avoiding collisions is also an important objective in the well trajectory design process. Moreover, designing well trajectories is a challenging and time-consuming process. Such designs demand multiple interdependent iterations to arrive at a required cost-effective solution that meets the drillability and safety requirements. In this study, we developed a novel framework to automate the well trajectory design process, including a well trajectory optimization technique to generate safer and more economical well trajectories. The developed framework was tested on two live oil- and gasfield cases. The first field case involved designing a single well trajectory in a crowded field, and the second field case involved designing 36 well trajectories in a field that consists of 13 legacy wells. The developed framework has a high impact on reducing the well trajectory design time and drilling length of trajectories. For example, in the second field case study, the obtained solution resulted in total length savings of 2.3% compared to an existing industry-standard tool solution, and it avoided collision with 13 wells. Also, the total time taken for designing 36 collision-free optimized wells was reduced from months (as per the industry standard) to a couple of days.

Summary Due to the nature of drilling operations, there are several companies collecting data at the rig. The data acquisition system of each company applies its own timestamp to the data. Subsequent aggregation of data (for example, in a data repository) relies on synchronized timestamps applied to the different data sources to correctly collate the data. Unfortunately, synchronized timestamping is rarely achieved. In this paper, we document the different sources of errors in timestamping of data and provide guidelines to help mitigate some of these causes. There are many reasons for the unsynchronized timestamping of data from different sources. It can be as simple as clock synchronization at the rig; each data-providing or -producing company has an independent clock. It can also be due to where the timestamp is applied, for example, at the data source or on data reception. Additionally, it can be due to how the timestamp is applied—at the start of the sampling interval, the midpoint, or the end. Some of the communication methods used at the wellsite, such as mud pulse telemetry that is used to transmit downhole measurements to the surface, have a high, nonstationary latency and the actual acquisition time may vary significantly from the received time. Not correcting the reception time for the transmission delay can result in erroneous timestamping of downhole-acquired data. Timestamping of derived data (data computed from two or more sources) is problematic if the data sources are unsynchronized. Synchronization of clocks within the data acquisition network is therefore extremely important. The resolution of time synchronization depends on purpose; motion control of the rig equipment (for example, the hoist) demands high-resolution timekeeping. However, for the purposes of timestamping acquired data, synchronization to a network time server (a computer with access to a reference clock that distributes the time of day to its client computers over a network) with a resolution of 1 millisecond is sufficient. The issue is agreeing on the common source of time (the reference clock) and agreeing on the passage of time signals through network firewalls. Timestamping is a more involved matter, calling for agreement on standards and, if possible, a computer-interpretable description of the time-related information associated with real-time data. In this paper, we describe in some detail sender vs. receiver timestamping, the downhole to surface timestamp chain, and timestamping of derived data. Systems automation and interoperability at the rigsite—allowing plug-and-play access to equipment and applications—rely on an agreed-upon network synchronization scheme and timestamping methods and standards. Indeed, designing applications that must handle uncertain time adds considerable complexity and cost, not to mention the impact on accuracy and reliability. We present an ordered approach (or guidelines) to a quite resolvable problem. In the last section of the paper, we use a semantic network approach (a semantic graph) to describe relationships for clock synchronization and timestamping (the guidelines and recommendations developed in this paper). A complete description of the semantic vocabulary is provided in an appendix. This makes these guidelines and recommendations digital—able to be interpreted by digital devices—and therefore implementable and auditable.