SummaryThe main objective of this paper is to present and discuss the results and significant observations gathered during 13 experimental runs conducted in a full-scale test well at Louisiana State University (LSU). The other two objectives of this manuscript are to show the use of distributed fiber-optic sensing and downhole pressure sensors data to detect and track the gas position inside the test well during the experiments, and to discuss experimental and simulated data of the gas migration phenomenon in a closed well.An existing test well at LSU research facilities was recompleted and instrumented with fiber-optic sensors to continuously collect downhole data and with four pressure and temperature downhole gauges at four discrete depths within an annulus formed by 9 5/8 in. casing and 2 7/8 in. to a depth of 5,025 ft. A chemical line was attached to the tubing allowing the nitrogen injection at the bottom of the hole. The research facilities were also equipped with a surface data acquisition system. The experiments consisted in injecting nitrogen into the test well filled with water by two means: either injecting it down through the chemical line or down through the tubing to be subsequently bullheaded to the annulus. Afterward, either the nitrogen was circulated out of the well with a backpressure being applied at surface to mimic a managed pressure drilling (MPD) operation or left to migrate to the surface with the test well closed.During the runs, the three acquisition systems (fiber optic, downhole gauges, and surface data acquisition) recorded all relevant well control parameters for a variety of gas injected volumes (2.0–15.1 bbl), circulation rates (100–300 gal/min), and applied backpressures (100–300 psi). The experimental results gathered by the acquisition systems were very consistent in measuring gas velocities inside the well. The numerical model predictions matched very close to the pressure behavior observed in the experimental trials. In the gas migration experiments, it was observed that the stabilized casing pressure at the end of gas migration is less than the initial bottomhole pressure, and it is a function of the volume of gas injected in the well. These facts are supported by the numerical simulation results.In this paper, we show the possibility of the use of fiber-optic and downhole pressure sensors information to detect and track the gas position inside a well or the marine riser during normal or MPD operations. Additionally, the vast amount of experimental data gathered during the experiments in which the nitrogen was left in the closed well to migrate to surface helped shed light on the controversial issue concerning the surface pressure buildup while the gas migrates to surface in a closed well. Numerical simulations were all instrumental for supporting the findings.
By creating a reverse deflection, negative-displacement horizontal well technology can successfully address the issue of construction challenges brought on by too little displacement in front of the horizontal well target. However, conventional horizontal well casing string running models are unsuitable for negative-displacement horizontal wells. This is because conventional horizontal well models assume that the casing string is in contact with the lower side of the borehole, which is inconsistent with the actual situation in negative -displacement horizontal wells. In this study, we examine the effects of fluid viscous resistance and internal and external fluid interaction forces on the running of the casing string using the Gaussian method and the complementary surface equivalence method. Based on the bending beam theory, we establish a deflection model to examine the interaction between the casing string and the borehole wall in the bending section. The friction and hookload calculation models of each well section and the strength check models are also established. The calculation results show that, according to the aforementioned model, there is an average error of 7.5% between the measured data and the calculated frictional force of the H1 and H2 negative-displacement horizontal wells. This error is within the reasonable range of field application and attests to the validity of the theoretical model. Finally, we study the influence of running factors on running ability and strength of casing string using the control variable method. The results indicate that the weight of the casing string is positively correlated with the variation law of the running ability of the casing string, the maximum offset distance and the length of the horizontal section are negatively correlated with the running ability of the casing string, and the borehole curvature is positively correlated with the variation law of the connection stress of the internal and external threads of the casing string. Therefore, in the process of running the cas-ing string in negative-displacement horizontal wells, using heavier casing string and reducing the maximum offset distance and horizontal section length can improve the running ability of the casing string.
A crucial step in new product development is the design verification process (DVP), which assures that the conceptual design of new technology is successfully transformed into a prototype product. In this paper, a novel DVP for subsea electrical and electronic prototype products is introduced. The comprehensive DVP ensures that the prototype of the product is designed righteously, is well documented, and is verified according to international practices and standards such as The American Petroleum Institute (API), International Organization for Standardization, European Norm, and European Union specifications. Compliance testing [environmental stresses and electromagnetic compatibility (EMC)] on the product is proposed to achieve Technology Readiness Level 2 (TRL2) of the first article of a printed circuit board (PCB). Concerning the quality of an electronic circuit board, it is recommended in this paper to follow the guide-lines and specifications of the Institute of Printed Circuits to certify the robustness of the design. Finally, the proposed DVP is thoroughly exercised on the insulation monitoring PCB under the prescribed test methods as a case study. The experimental results show that the design is robust and the product is suitable for use in the intended subsea environment after further raising the technology maturity level of the product.
SummaryThe experimental design of well cement with durable compressive strength (CS) is challenging and time-consuming. The current research predicts CS using the enhanced group method of data handling via a modified Levenberg-Marquardt algorithm (GMDH-LM) with experimental data. Class F fly ash (CFFA) is used as a supplementary material to cement at various proportions. Experimental tests of CS, thermogravimetric (TG) analysis, rheology, and scanning electron microscopy (SEM) are applied. Experimental findings revealed that the addition of fly ash (FA) enhances CS with curing time as an outcome of pozzolanic action. CS for 20% FA reinforcement after curing for 28 days was 42.95 MPa, compared with 41.53 MPa for 50%. This indicates that a higher addition of FA lowers CS. The rheological findings revealed that FA enhanced the viscosity of the cement slurry. The SEM images demonstrated that the incorporation of CFFA with cement modified the contexture of hardened cement. Cement, water, oilwell cement (OWC), curing time, dispersant, and FA were assigned as input variables for GMDH-LM while CS from the experimental analysis was set as output. Machine learning (ML) findings indicated that GMDH-LM can effectively estimate the CS of OWC. GMDH-LM performed better than backpropagation neural network (BPNN), support vector machine (SVM), and normal GMDH models in predicting CS; it provided higher linearity during training as GMDH-LM gave R2 = 0.958, GMDH = 0.946, SVM = 0.925, BPNN = 0.897, and the least loss functions of mean square error (MSE) = 0.238, MSE = 1.685, MSE = 2.567, and MSE = 4.032, respectively. Similarly, good results were ascertained during testing GMDH-LM provided R2 = 0.928, GMDH = 0.907, SVM = 0.895, BPNN = 0.878, and the lowest loss functions of MSE = 0.304, MSE = 2.650, MSE = 3.494, and MSE = 5.678, respectively. Therefore, the comparative results of all experiments and predictions reveal that GMDH-LM can be deployed as an advanced approach for the estimation of cement hydration in oil and gas wells.
The Assisted Cement Log Interpretation Project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process.The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals. By using high- quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time.The tool consists of a training and a prediction tool integrated with cased- hole logging interpretation software. By containerizing the code using an "API First" design principle (API: application programming interface), the applicability of this add- on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further.To improve cement log interpretation consistency in the industry, the code will be made available as open source.
It is shown how the flow from pumping cement through an open -ended pipe very quickly changes direction and the cement flows upward. This rapid change in flow direction indicates that a diverter tool, which leads the cement slurry perpendicularly out of a closed -ended pipe, does not have any function. The placement of a balanced plug is feasible. However, a high-density fluid above a lighter fluid is not stable. The phenomenon is known as Rayleigh-Taylor instability. In principle, to be reasonably stable, the interface must be horizontal. The longer the interface is, the more unstable is this case. Thus, it is difficult, or sometimes impossible, to create a stable situation in a deviated well section, especially if the well section diameter is large. Observations show that it is possible to modify density differences, thickening time, and viscosity differences such that the success rate can be between 40% and 60%. Using a floatable cement foundation tool, this success rate increased to more than 95% in North Sea applications. The use of such a tool is described, and its performance is justified by a numerical analysis of cement flow.
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.
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.
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 pre-sented. 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.
Due to the nature of drilling operations, there are several companies collecting data at the rig. The data acquisition system of each com-pany 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.
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.
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.
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.
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 dual- inclination (DI) corrections to improve both azimuth and inclination accuracy. A second wellbore position is calculated using gyroThe 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.
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 com-parisons, 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 circula-tion 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 Studies have shown that achieving a consistent perforation hole size in casing (i.e., entry hole) and zero-phase perforation gun orientation led to improved treatment distribution among multiple perforation clusters in plug-and-perf limited entry treatments. In addition to reducing variation in the perforation entry hole by establishing uniformity in gun clearance and the angle of incidence of the perforation jet at the wall of the casing, oriented perforating has been shown to minimize the tendency of proppant to separate from the fracturing fluid while traveling across the perforated intervals (inertial effect) and mitigate nonuniform entry hole erosion due to gravity-induced proppant stratification. The primary goal of this study was to determine the controllable perforating gun elements and accessories that effect the accuracy of gun orientation and entry hole dimensions. Surface tests were conducted at manufacturing facilities for determining the characteristics of the entry holes in pipe produced by shaped explosive charges using various system configurations and the robustness of various gun orientation devices. Promising perforating systems were then used in wellbores to create calibration entry holes (downhole tests) that were measured for equivalent diameter and orientation accuracy using high-resolution acoustic imaging before conducting treatments. This process enabled components of the perforating system influencing entry hole size and gun orientation to be evaluated and modified, as necessary. Elements of the perforating system and downhole environment that influenced entry hole size and consistency included casing type, cement sheath characteristics, perforating gun clearance and orientation, perforating charge type and density, packing arrangement of multiple charges, charge tube and charge carrier design, gun detonation system, hydrostatic pressure, and locking devices. Achieving tight control of these elements significantly reduced variation in entry hole size. Deviations from surface and downhole testing results were commonly attributed to using perforating system elements in the field that differed from those used by the manufacturer in surface testing. Factors affecting gun orientation accuracy and consistency included weight bar type, gun string length, weight, and stiffness, the presence of modified standoff bands, progressive gun deformation during firing, wellbore tortuosity, and self-orienting devices. Several orientation systems were found that achieved orientation within the target 20°-window. To assess the value of this workflow process, the paper includes information on the results of diagnostic tests for evaluating the accuracy of the ultrasonic measuring device, the derivation process used for determining coefficients for a two-component perforation erosion model, and the use of the derived erosion rate coefficients for computing the mass of proppant that enters each perforation and perforation cluster during a fracturing treatment.
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.
Erosion and forces on rams may prevent a blowout preventer (BOP) from sealing a well. Analyzing the flow field throughout a BOP may provide insight into these flowing effects on the inability of a BOP to seal the well. 3D transient simulation of fluid flow throughout closing BOP fluid domains is demonstrated using computational fluid dynamics (CFD). Simulation may be used to analyze the transient stress, pressure, and velocity fields throughout a BOP domain as it is closing. Many challenges exist in simulating a closing BOP using CFD, including boundary conditions and treatment of dynamic meshing. Solutions to those challenges are presented in this work. CFD simulations are carried out using ANSYS Fluent v19.2 (ANSYS, Canonsburg, Pennsylvania, USA). For inlet boundary conditions to the CFD domain, the CFD simulations are explicitly coupled with a 1D wellbore simulator. The 1D wellbore simulator provides a connection between the BOP and constant pressure reservoir. Numerical instability is present during this coupling process. An implementation for dealing with this instability is presented. An example validation case is presented to demonstrate the accuracy of CFD for pressure fields throughout valves. A second 2D axisymmetric case is shown to demonstrate the meshing and coupling simulation process. A third case, simulation through a 3D shear geometry is then presented to show the applicability of the process to a more complex geometric design. Velocity and stress fields are plotted to show the practicality of CFD in analyzing the probable causes of failure in BOP closures.
Summary Steering drilling is used for exploring oil, natural gas, and other liquid and gaseous minerals. Steering drilling consists of high-efficiency drill bits, steering power drilling tools, and logging while drilling (LWD) and is used in petroleum drilling engineering. This paper mainly discusses subhorizontal drain geosteering, one of the methods of guided subhorizontal drilling. We use the currently popular deep learning method to conduct intelligent guided drilling. Geosteering is a sequential drilling decision process under uncertain stratum environment. However, the current geosteering drilling process relies heavily on manual work and has no use of temporal context. This paper aims to solve decision-making of geosteering in deep well (between 4500 and 6000 km) or ultradeep well (between 6000 and 9000 km). To this end, we make three contributions: (1) a wide-angle eye mechanism to obtain more geological information; (2) an asymmetric peephole convolutional long short-term memory (APC-LSTM) approach for geosteering drilling decision, whose input data were assembled with the wide-angle eye mechanism; and (3) use of the deep convolution generative adversarial networks (DCGAN) model to generate simulated logging data and conduct experiments in the simulation environment to verify our proposed method. APC-LSTM can capture the spatial-temporal correlation better between different strata for decision-making. Meanwhile, the APC-LSTM drilling decision model achieved better performance than other advanced methods in two drilling data sets. Tested in a simulative drilling environment, our proposed model achieves excellent application effect. Moreover, our method has been applied to the wells of oil field in practice.
