Downhole Sensors Research Articles

Hydraulic modeling and real-time optimization of drilling fluids: A future perspective

The evolution of drilling operations in the oil and gas industry has highlighted the need for more efficient and adaptive management of drilling fluids, particularly in complex well environments. This paper presents a comprehensive review of hydraulic modeling and real-time optimization of drilling fluids, focusing on future perspectives and industry implications. Hydraulic modeling plays a crucial role in predicting pressure, fluid flow, and wellbore stability, while real-time optimization, powered by advanced sensors, AI, and machine learning, enables dynamic fluid management. The integration of these technologies allows for continuous monitoring and adjustment of fluid properties, improving efficiency, reducing downtime, and preventing formation damage. Future technological advancements, including digital twins and enhanced downhole sensors, are expected to improve well performance and cost-efficiency further. Strategic recommendations for industry adoption include investment in advanced technologies and further research into hydraulic modeling and real-time optimization for complex wells.

Innovative approaches in enhanced oil recovery: A focus on gas injection synergies with other EOR methods

Enhanced Oil Recovery (EOR) techniques have become crucial in extending the productive life of mature oil fields and maximizing resource extraction. Among these, gas injection methods, particularly those involving CO₂, nitrogen, and hydrocarbon gases, have shown significant promise. This review focuses on the innovative synergies achieved by combining gas injection with other EOR methods to enhance recovery efficiency and economic viability. Gas injection techniques work by increasing reservoir pressure and reducing oil viscosity, facilitating easier flow and extraction. When integrated with other EOR methods such as thermal, chemical, and microbial techniques, the synergistic effects can lead to even greater enhancements in oil recovery. For instance, the combination of gas injection with thermal EOR, like steam injection, can improve the displacement of heavy oils by simultaneously reducing viscosity and enhancing thermal conductivity within the reservoir. Similarly, integrating gas injection with chemical EOR methods, such as polymer flooding, can optimize the sweep efficiency by leveraging the mobility control provided by polymers and the pressure maintenance by gases. Recent advancements in monitoring and simulation technologies have further improved the effectiveness of these combined approaches. Real-time reservoir simulation and 4D seismic monitoring allow for precise tracking of gas and fluid movements, enabling dynamic adjustments to injection parameters for optimal performance. Smart well technologies, equipped with advanced downhole sensors, provide continuous data on reservoir conditions, facilitating more responsive and adaptive EOR strategies. However, these innovative synergies are not without challenges. Economic feasibility remains a significant concern, particularly given the high costs associated with gas procurement and the integration of multiple EOR techniques. Additionally, managing the complex interactions between different EOR methods and heterogeneous reservoir conditions requires sophisticated modeling and robust operational protocols. In conclusion, the integration of gas injection with other EOR methods offers a promising pathway to enhance oil recovery rates and extend the lifespan of mature reservoirs. Continued research and development, coupled with cross-industry collaboration, are essential to overcoming the economic and technical challenges associated with these innovative approaches. This review underscores the potential for gas injection synergies to revolutionize EOR practices and drive sustainable growth in the oil and gas industry.

Robust CO2 plume imaging by joint tomographic inversion using distributed pressure and temperature measurements

The scientific community has become increasingly interested in geological CO2 sequestration and CO2 enhanced oil recovery (EOR). The tracking of the CO2 propagation in both space and time during geologic sequestration is necessary to ensure the secure and effective handling of a site for CO2 injection. Our objective is to develop efficient and novel models and monitoring techniques for visualizing CO2 plumes using field measurements. As a first step, the streamline-based data integration approach is extended to include data from distributed temperature sensors (DTS). The DTS and pressure data are then jointly history matched using a hierarchical workflow combining evolutionary and streamline methods. As a final step, we will create maps that visualize CO2 propagation during the sequestration process based on saturation and streamline maps. We validate the extended streamline-based inversion method using a synthetic model. An application of the hierarchical workflow is then made to the CO2 geologic storage test site in Michigan, USA. Monitoring data includes bottom-hole pressure of the injection well, DTS data at the monitoring well, and distributed pressure measurements from several downhole sensors along the monitoring well. Based on the history matching results, the CO2 movement is largely limited to the zones intended for injection, which is in agreement with an independent warmback analysis of the temperature data. The novelty of this work is the extension of the streamline-based inversion algorithm for the DTS data, its field application to the Department of Energy regional carbon sequestration project, and potential extensions to other CO2-EOR and/or associated geological storage projects.

Research on Vibration Accumulation Self-Powered Downhole Sensor Based on Triboelectric Nanogenerators.

In drilling operations, measuring vibration parameters is crucial for enhancing drilling efficiency and ensuring safety. Nevertheless, the conventional vibration measurement sensor significantly extends the drilling cycle due to its dependence on an external power source. Therefore, we propose a vibration-accumulation-type self-powered sensor in this research, aiming to address these needs. By leveraging vibration accumulation and electromagnetic power generation to accelerate charging, the sensor's output performance is enhanced through a complementary charging mode. The experimental results regarding sensing performance demonstrate that the sensor possesses a measurement range spanning from 0 to 11 Hz, with a linearity of 3.2% and a sensitivity of 1.032. Additionally, it exhibits a maximum average measurement error of less than 4%. The experimental results of output performance measurement indicate that the sensor unit and generator set exhibit a maximum output power of 0.258 μW and 25.5 mW, respectively, and eight LED lights can be lit at the same time. When the sensor unit and power generation unit output together, the maximum output power of the sensor is also 25.5 mW. Furthermore, we conducted tests on the sensor's output signal in conditions of high temperature and humidity, confirming its continued functionality in such environments. This sensor not only achieves self-powered sensing capabilities, addressing the power supply challenges faced by traditional downhole sensors, but also integrates energy accumulation with electromagnetic power generation to enhance its output performance. This innovation enables the sensor to harness downhole vibration energy for powering other micro-power devices, showcasing promising application prospects.

Direct Evidence for Diverse Source Complexity in Small Earthquakes (Mw 3.3–5.0) Obtained from Near-Source Borehole Seismic Data

Abstract Small earthquakes (Mw <5) may have a similar degree of complexity as large earthquakes. However, their seismic waveforms are strongly distorted during wave propagation, making their complexity challenging to resolve. In many cases, the source parameters of small events are determined based on models that assume their source patterns are simple. In this study, to directly examine the source complexities in small events, we examined high-quality near-source (<8 km) seismic waveforms recorded by two excellent downhole sensors in Japan. The results show that the P waveforms of microearthquakes (Mw <2) are always simple at the sensors and agree well with the synthetic waveforms based on a 1D structure up to 20 Hz. The microearthquake waveforms in this frequency band essentially represent path effects besides the static source effect, suggesting that the contribution of structural inhomogeneity to the observed waveforms is small. Taking advantage of this, we inferred the moment rate functions of 164 Mw 3.3–5.0 events from the shapes of the direct P waves. They showed diversity in their complexity, and even conservatively estimated, 25% of the events had multiple subevents. The results suggest that methods that account for complexity, rather than those that assume a simple source pattern, are required to characterize even small events.

Spectral ratio analysis of the site amplification effect and nonlinear site response at MYGH10 during the 2021 MW 7.1 Fukushima earthquake in Japan

AbstractGround motion recording with peak ground acceleration (PGA) exceeding 1.43 g was observed at the Yamamoto (MYGH10) station during the MW 7.1 Fukushima earthquake off the east coast of Honshu, Japan, in February 2021. In this study, we investigated the site amplification effect and nonlinear site response at the MYGH10 site using the horizontal‐to‐vertical spectral ratio (HVSR) and surface‐to‐borehole spectral ratio (SBSR), respectively. The site transfer function of station MYGH10 was presented using HVSRs (HVSR on the ground surface) and SBSR at different frequency bands. The dominant contributing frequency band of the PGA was determined by increasing the high cutoff frequency of the bandpass filter when filtering the acceleration recording, which was used to interpret the anomaly of the PGA by combining it with the site characteristics. We found an obvious site effect below the downhole sensor through the HVSRb (HVSR on the downhole bedrock) and determined the influence of the frequency band of the downhole site on ground motion. In addition, substantial discrepancies in the frequency and amplitude between the spectral ratio curves for the Fukushima mainshock records and the weak historical motions were identified. The calculated values of the degree of nonlinearity suggested that a strong nonlinear site response occurred at station MYGH10. Finally, the recovery of the site after strong shaking was evaluated by using the average spectral ratio curves of several aftershock records.

Mine wind speed sensor location selection based on decision tree theory

Abstract In order to realize the scientific layout of underground roadway sensors, based on the information entropy theory, the decision tree algorithm is applied to the location selection of the underground wind speed sensor. Taking the air volume traversing the entire network branch as the goal, the node air volume balance and the characteristic relationship between air volume and wind pressure as the constraints, the conventional layout data of the mine wind speed sensor is selected as the training sample, and the reasonable decision node is selected by measuring the uncertainty of the characteristic attribute. The research shows that the smaller the information entropy of the layout elements, the greater the weight in determining the sensor location. The classification conditions obtained by the wind speed sensor are, in descending, order: airflow disturbance, roadway support, distance from the inlet (return) and outlet, and roadway type. Algorithms are applied to effectively combine downhole sensor siting with historical data containing unambiguous results.

CALCULATION OF TECHNOLOGY ALGORITHM AND SUCCESSFUL APPLICATION OF THERMOCHEMICAL METHOD DURING WELL WORKOVER

This article is devoted to the analysis of the success of the thermochemical method of generating and transferring thermal energy for underground workover technologies of producing wells. In order to justify the design and consumption of reagents, an algorithm and calculator were compiled based on the well design and thermophysical properties of consumables and fluids: heat capacity of steel, water, ice, enthalpy of ice melting and chemical reaction, the length of the paraffin hydrate plug interval.Thermophysical calculations have shown that for a well with a diameter of 146 and a 73 mm tubing lift, 153 kg of fuel reagent is required for thermal destruction of a paraffin hydrate plug 100 meters long. Based on the geometry of the candidate well and the expected intervals of waxhydrate plug formation, reagent consumption is determined, and a design and work plan are drawn up. The algorithm makes it possible to calculate the flow rate and concentration of working solutions of reagents in order to reach the target temperature at the thermal exposure interval.Measurements of the deep thermometer made it possible to determine that the selected composition of reagents and the exothermic reaction with a delay in heat generation allow to achieve a loss of heat to dissipation at the level of 17 % by 2500 meters of the tubing elevator.The technology of thermal destruction of paraffin-hydrate plugs in gascondensate wells was implemented at the East-Tarkosalinskoye oil and gas condensate field and the Komsomolskoye field with achievement of the specified success criteria.Technologies for development and removal of water from the bottomhole of gas and gas-condensate wells ensure formation of a gas-liquid mixture and removal of water from the bottomhole at the wellhead. The technologies were used at the production facilities of the Vuktyl field (Komi Republic), Amangeldygaz (Kazakhstan Republic), and the East-Tarkosalinsky field (Yamalo-Nenets Autonomous Okrug). As a result of water development, gas wells are switched from pulsating to permanent operation. Heat (thermal) treatment of producing wells uses injection of heat and nitrogen-generating solutions with transfer of thermochemical energy of reagents through permafrost and low-temperature intervals with minimal heat losses. The efficiency of the heat transfer technology was confirmed by temperature measurement by downhole sensors at 139–147 °C at development facilities with reservoir temperatures of 41–43 °C (Ural-Volga region, Purovsky region).The technology of restoring the productivity of gas and gas condensate wells using aerated acids makes it possible to increase the success of subsequent development. The use of the technology of foam acid treatment with methanol makes it possible to successfully restore the productivity of gas and gas condensate wells prone to water removal. Aeration and heating of process solutions of reagents makes it possible to carry out comprehensive measures for treatment of the bottomhole zone of the well with acid and thermochemical impact.

In-Situ Bubblepoint Measurement by Optical Spectroscopy

Summary Representative fluid properties are required for a wide range of field life aspects such as initial sizing of reservoir hydrocarbon reserves and production planning. Fluid properties are routinely obtained from laboratory sample analysis, but some fluid properties can also be measured in situ with formation testers. A new downhole bubblepoint technique has been developed to supplement traditional downhole fluid analysis (DFA) measurements. Bubble-initiation pressure is measured on reservoir fluids enabling early estimations and sample representativity. The method outlined consists of two parts—bubble generation and bubblepoint-pressure detection. After the isolation of a volume of contamination-free fluid in the fluid analyzer module of a formation tester, a downhole pump is used to reduce flowline pressure at a low and precise flow rate. Bubble initiation is detected using optical spectroscopy measurements made at a 128-ms data sampling rate. Even very small bubbles scatter visible and near-infrared light directed through the flowline, ensuring that the initiation of bubbles is detected. Flowline decompression experiments are performed in minutes, at any time, and on a range of downhole fluids. Downhole bubblepoint pressure measurements were made on four different fluids. The gas/oil ratio (GOR) of the tested fluids ranged from 90 m3/m3 to 250 m3/m3. In each case, the downhole bubblepoint obtained from the flowline decompression experiment matched the saturation determined by constant composition expansion (CCE) in the laboratory to within 350 kPa. We observed that bubble initiation is first detected using near-infrared spectroscopy. As the pressure drops, gas bubbles coming out of the solution increase in size, and the bubble presence becomes identifiable on other downhole sensors such as the live fluid density and fluorescence, where it manifests as signal scattering. For each of the investigated fluids, pressure and density measurements acquired while the flowline pressure is above saturation pressure are also used to compute compressibility as a function of pressure. This downhole bubblepoint pressure measurement allows optimization of real-time sampling operations, enables fluid grading and compartmentalization studies, and can be used for an early elaboration of a fluid equation-of-state (EOS) model. The technique is suitable for black oils and volatile oils. For heavy oil with very low gas content, the accuracy of this technique may be reduced because of the energy required to overcome the nucleation barrier. Prior documented techniques often inferred downhole bubblepoints from the analysis of the rate of change of flowline pressure. Direct detection of the onset of gas bubble appearance without requiring additional dedicated downhole equipment and validated against laboratory measurements is shown for the first time. The measurement accuracy is enabled by the combination of 128-ms optical spectroscopy with low and accurate decompression rates.

Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section.

The downhole vibration is one of the most crucial factors that affect downhole equipment performance and failure, besides wellbore instability. Downhole tool failure, hole problems, mechanical energy loss, and ineffective drilling performance are commonly associated with drillstring high vibration levels. The high vibration level will lead to more complications while drilling that might cause nonproductive time and extra cost. Meanwhile, the downhole sensors for detecting the drillstring vibrations add more cost to the operation. Consequently, the new solutions based on technology capabilities provide a powerful tool to integrate and interpret the drilling data for the best use of @@the data for operation performance enhancement. This study provides a successful application for utilizing the surface drilling data to automate drillstring vibration detection during the drilling curve section employing machine learning (ML) techniques. The axial, torsional, and lateral vibration modes are detected through testing four ML techniques named the @@adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF), functional networks (FN), and support vector machines (SVMs) with real field data. The models' development was achieved by comprehensive study starting from data gathering, wrangling, statistical analysis, developing the ML models, evaluating the model prediction accuracy, and reporting the high accuracy results. The developed models were evaluated, and results showed that ANFIS and SVM models provided the highest accuracy with a coefficient of correlation (R) ranging from 0.9 to 0.99 followed by the RBF and FN models through model training and testing (R ranging from 0.82 to 0.96). Validating the models over unseen data confirmed the high accuracy prediction for the three vibration modes. Generally, the developed models provided technically accepted accuracy with R higher than 0.93 and AAPE less than 2.8% for SVM and ANFIS models while FN and RBF showed R between 0.82 and 0.95 and AAPE less than 5.7% between actual readings and predictions. Based on these results, the developed ML algorithm might be utilized as an intelligent solution to autodetect downhole vibration while drilling from surface sensor data only, which will save the downhole tool cost.

Focused Reservoir Fluid Sampling Uses Artificial Intelligence Technology

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 210091, “Optimizing Focused Reservoir Fluid Sampling Using a Deterministic Causation Artificial Intelligence Intuition Technology,” by Rabindra Chakraborty, Senslytics; Chengli Dong, Shell; and Hani Elshahawi, SPE, NoviDigiTech, et al. The paper has not been peer reviewed. _ Samples collected using wireline formation testing (WFT) provide vital information throughout the lifetime of a reservoir. Contaminated samples can lead to erroneous fluid analysis results with potentially huge economic consequences. A need exists for an application that can assist engineers in accurately inferring the state of fluid contamination. The complete paper describes the development of a WFT contamination-forewarning application based on a framework that advises real-time decisions regarding the state of fluid contamination and recommending changes that will help optimize the WFT operation. Focused Fluid Sampling During the past decade, focused fluid sampling has emerged as a viable alternative to conventional formation-fluid sampling. The method uses a special probing tool containing two distinct flow areas. The central (sample) inlet is connected to a sample line, often with an associated pump, while the peripheral (guard) ring is also associated with a dedicated pump. Focused sampling usually results in faster cleanup but comes with increased operational complexity. The flow rate of the sample and guard lines must be properly adjusted and synchronized for each case at hand, and the interpretation of downhole measurements on both sides is significantly more complex than for conventional sampling operations. The success of fluid-sampling operations is highly dependent on human interpretations of the real-time downhole sensor data. Focused sampling is carried out by manipulating the differential rates of sample and guard pumps to stimulate and accelerate sample cleanup. Various types of flow regimes observed during focused sampling operations may be classified as follows: - Idle: During such regimes, both sample and guard pumps are turned off. Thus, no cleanup takes place, and fluids in the tool’s sample lines that are detected by the downhole analyzers remain stagnant. - Commingled: During commingling, either the sample pump or the guard pump is running, but flow occurs through both the sample and guard areas combined. - Split flow: In such periods, both sample and guard pumps run simultaneously but at different flow rates, with fluids moving at different speeds through the sample (central) and guard (outer) areas. The physics of fluid flow in near-wellbore porous media are highly complex and challenging to compute analytically and difficult to numerically simulate at a relevant scale, especially in the context of real-time operations. The authors have developed what they term “intuition technology,” an artificial-intelligence paradigm that uses scientific learning as its fabric and mimics the human decision-making process by iterating hypotheses and weighing situational changes surrounding dynamics simultaneously while keeping an eye on possible asymptomatic behavior. The process involves harnessing experts’ subtle knowledge of fluid properties and change behavior as hypotheses and iterating them with the situational and time-series data to generate a scientific fabric that governs the cleanup. A contamination-forewarning application built on this intuition framework was able to interpret the fluid-cleanup state successfully in all examples examined as part of this pilot.

A new approach for real-time prediction of stick–slip vibrations enhancement using model agnostic and supervised machine learning: a case study of Norwegian continental shelf

Efficient and safe drilling operations require real-time identification and mitigation of downhole vibrations like stick-slip, which can significantly diminish performance, reliability, and efficiency. This pioneering research introduces a robust machine learning approach combining model-agnostic regression techniques with Bayesian Optimized Extra Trees (BO_ET) to accurately predict stick-slip events in real-time using downhole sensor data. The model is rigorously tested and validated on a substantial offshore dataset comprising over 78,000 data points from a Norwegian continental shelf (NCS) oil field. The key input features encompassing real-time downhole and surface drilling parameters are carefully selected, including critical variables such as collar rotational speed, shock risks, annular pressure, torque, mud flow rate, drill string vibration severity, and other relevant measurements. These parameters offer significant insights into the occurrence of harmful stick-slip vibrations. Among several sophisticated machine learning models, the Extra Trees (ET) algorithm demonstrates superior performance with the lowest errors of 5.5056 revolutions per minute (r/min) Mean Absolute Error (MAE) and 9.9672 r/min Root Mean Square Error (RMSE) on out-of-sample test data. Further hyperparameter tuning of the ET algorithm via Bayesian Optimization dramatically reduces errors down to 0.002156 MAE and 0.024495 RMSE, underscoring the significant innovation and advantages of the proposed approach. By seamlessly incorporating real-time downhole sensor data and drill string mechanics, the model enables reliable identification of stick-slip events as they occur downhole. This grants opportunities to optimize critical drilling parameters including revolutions per minute (RPM), weight-on-bit (WOB), mud flow rates, and more to effectively mitigate stick-slip severity and improve the rate of penetration (ROP). Integrating the approach into automatic driller systems on offshore rigs offers immense benefits for drilling operations through substantially increased efficiency, fewer premature failures, lower costs, and significantly improved productivity and safety. Overall, this research strongly emphasizes the immense transformative potential of advanced data analytics and machine learning in enabling more efficient, economical, and sustainable drilling practices. The proposed model demonstrates clear superiority over existing methods and establishes a robust and reliable platform for real-time stick-slip prediction and mitigation, maximizing drilling performance.Graphical abstract

Hard-Rock κ0 at KiK-Net Sites in Japan

ABSTRACT Site-specific seismic hazard studies require the knowledge of the shear-wave velocity VS and the high-frequency site attenuation parameter κ0 at the reference rock level at depth. The latter one (called κ0,ref) is often not available and hard to derive. In this study, we make use of the KiK-net database in Japan that consists of surface and colocated downhole sensors. We select 175 sites where the bottom sensor is deployed at rock or hard-rock conditions resulting in a database with many recordings at VS≥1500 m/s. This allows us to tackle two questions: first, is it possible to derive κ0,ref from surface recordings? Second, does κ0 reach an asymptotic level at high VS that could be used as a κ0,ref in site-specific seismic hazard studies? Our results show that measures of κ0 derived from S and coda waves are equivalent. Thus, it is not possible to obtain κ0,ref from surface recordings using coda waves. On the other hand, S-wave measurements of κ0 from surface rock sites are close to κ0,ref if VS≥760 m/s or if the sedimentary cover is thin. The values of κ0 decrease with increasing VS and reach an asymptotic value. The scatter in the so obtained κ0,ref is high, but it can be reduced by selecting subregions with similar geological conditions. Finally, we observe that borehole and surface κ0 are correlated, and that the variability of κ0,ref is only slightly reduced compared to κ0 at the surface. Although we cannot exclude any influence of source effects, our findings indicate that κ0,ref has to be considered as a deep site parameter.

A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time.

When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close to each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models named ECDeffc.m and MWeffc.m were developed to predict ECD and mud weight (MW) from surface-drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation coefficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 1.66%. The two novel models were compared with artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value obtained from pressure-while-drilling (PWD) tools. These models can be utilized during well design and while drilling operations are in progress to evaluate and monitor the appropriate mud weight and equivalent circulation density to save time and money, by eliminating the need for expensive downhole equipment and commercial software.

A triangular electrode triboelectric nanogenerator for monitoring the speed and direction of downhole motors

Downhole motors are essential in providing rotational power to the drill bit and achieving the desired trajectory of the wellbore, making them the most reliable component of the bottom-hole assembly. Monitoring their speed and direction is critical to improving drilling efficiency. In this paper, we propose the use of a triangular electrode triboelectric nanogenerator (T-TENG) for monitoring downhole motor speed and direction. The T-TENG employs triangular copper foils as the electrode layer and can determine the direction of rotation based on the transient characteristics of the output signal. Experimental results show that the T-TENG exhibits a measurement error of less than 3 % for speeds within the monitoring range of 0–1200 rpm, and demonstrates good linearity. To enable real-time detection of the downhole motor's instantaneous speed, we introduce an instantaneous speed algorithm based on MATLAB. The average speed curve is obtained by fitting the instantaneous speed using variational mode decomposition (VMD) analysis. Furthermore, the T-TENG exhibits impressive power generation performance and can simultaneously power a low-power temperature and humidity sensor and 436 series-connected light-emitting diodes (LEDs). Compared to conventional downhole sensors, the T-TENG obviates the requirement for an external power source and enables simultaneous measurement of speed and direction. Hence, it offers a fresh perspective for the advancement of next-generation downhole sensors.

Detecting downhole vibrations through drilling horizontal sections: machine learning study

During the drilling operations and because of the harsh downhole drilling environment, the drill string suffered from downhole vibrations that affect the drilling operation and equipment. This problem is greatly affecting the downhole tools (wear and tear), hole problems (wash-out), mechanical energy loss, and ineffective drilling performance. Extra non-productive time to address these complications during the operation, and hence, extra cost. Detecting the drillstring vibrations during drilling through the downhole sensors is costly due to the extra service and downhole sensors. Currently, the new-technology-based solutions are providing huge capabilities to deal intelligently with the data, and machine learning applications provide high computational competencies to learn and correlate the parameters for technical complex problems. This research presented a successful case study for developing machine learning models through a comprehensive methodology process for vibration detection using surface rig data through data collection, preprocessing, analytics, training and optimizing the models’ parameters, and evaluating the performance to have the best prediction results. Evaluating the models’ performance showed that obtained predictions have a great match with actual measurements for the different stages of training, testing, and even during models’ validation with unseen well data. Real-field horizontal drilling data was utilized to feed and train the models through different tools named radial basis function (RBF), support vector machines (SVMs), adaptive neuro-fuzzy inference system (ANFIS), and functional networks (FN) to auto-detect the three types of downhole vibrations (axial, torsional, and lateral). The study results showed a high correlation coefficient (higher than 0.9) and technically accepted average absolute percentage error (below 7.5%) between actual readings and predictions of the developed ML models. The study outcomes will add to the automation process of drilling operations to avoid many tools failure by comparing predicted vibrations versus downhole tools limits such as red zone and continuing drilling without interruption to the well total depth especially while drilling horizontal sections.

A Self-Powered Multifunctional Sensor for Downhole Motor Based on Triboelectric Nanogenerator

The rotational state of the downhole motor is critical for monitoring its operational condition. In this work, a self-powered multifunctional sensor (hereafter referred to as SPMS) is proposed to monitor the rotational speed, angle, and direction of rotation of downhole motor without external power supply. The experimental results show that the SPMS can measure the rotational speed in the range of 0–750 r/min with an error rate of less than 2% and an excellent linearity. In addition to the speed measurement function, it can also measure the rotation direction and angle with a resolution of 22.5°. Furthermore, the possibility of the SPMS converting downhole motor rotation energy into electrical energy and powering other downhole devices is also explored. The experimental results show that the SPMS has an open-circuit voltage of about 1.2 kV and a peak power of 1.128 mW at 150 r/min. It can light up 601 series light-emitting diodes (LEDs) and force a low-power temperature sensor. Compared with traditional downhole sensors, the SPMS can achieve multiparameter measurement without external power supply, which expands the application range of downhole sensors and is more suitable for downhole working conditions.

System-Engineering Recommendations Aim To Improve Intelligent Completions

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 209965, “System Engineering Recommendations To Improve Intelligent Completions,” by Ron Nelson, SPE, Consultant; Eric Handley, MacDermid Offshore Solutions; and Jonathon Joubran, SPE, Halliburton, et al. The paper has not been peer reviewed. _ The complete paper compiles recommendations from a broad range of sources into a single document to aid future intelligent-completion installations and operations. While emphasis is placed on subsea completions, these recommendations can be applied readily to differing well types. Important differences between production wells and injection wells also are addressed. Introduction Intelligent-completion interfaces span drilling, completions, facilities, production operations, and subsea-systems-engineering disciplines. Affected system components include the tree and tubing hanger, control system, control fluid, control lines, well fluids, well casing, downhole sensors, feed-through packers, and interval control valves (ICVs). Example diagrams are provided in Appendix B of the complete paper. While a successful intelligent completion can enhance a well’s value significantly, a failed intelligent completion can severely diminish that value, possibly rendering the well inoperable. Proposed Intelligent-Well-Design Process Control-Fluid Selection. The design process should start with the control fluid, which provides hydraulic force to open and close the ICV. It traverses the entirety of the system and will affect most system components. A successful control fluid will accomplish the following goals, which are further detailed in the complete paper: - Provide adequate lubricity - Maintain required properties across all design temperatures - Be compatible with all materials - Be compatible with completion brines - Provide adequate hydrate inhibition - Minimize environmental impact If the intelligent completion is to be installed in an existing development already using a fluid that cannot be used for the ICV, it may be possible to fill or “stack” the ICV lines with another control fluid that meets the downhole requirements and also is fully compatible with the in-service control fluid. The fluid’s original equipment manufacturer (OEM) should be consulted when a mixed-fluid system is considered. Assessing Thermally Induced Pressure Gains. Once a preferred control fluid has been identified, the next recommended step is to assess worst-case thermally induced pressure changes in the control lines. Thermal effects directly affect numerous intelligent-completion components. API SPEC 17D recommends keeping the tree-mounted ICV control-line isolation valves [tree-isolation valves (TIVs)] closed when not shifting the ICVs. At the other end of the control line, the ICV’s seals isolate the control fluids from the well fluids. Each downhole control line is a closed system when the TIVs are closed. A closed control line will behave like a casing annulus in that internal pressure will rise when warmed and fall when cooled. The pressure changes in the lines, however, can be far greater than those seen in the casing annuli and can affect the intelligent completion system’s pressure requirements profoundly.

Application of artificial neural networks for stress state analysis based on the photoelastic method

The present article proposes an evolutionary development of the photoelasticity method for measuring stresses based on annular photoelastic sensors application along with stress pattern recording with the aid of a digital camera and its recognition using artificial neural networks. The analysis of the modern application of the photo-elasticity method for various problems within the theory of strength is presented. The principle of operation of photoelastic sensors based on the photoelasticity effect is considered. Optical patterns in an annular photoelastic sensor are presented for various values of the horizontal stress. The calculation of the stress state of the sensor for the following full-scale experiment has been performed, the estimate of the threshold conditions under which the sensor can be applied has been performed. As a result of a laboratory experiment, a dataset of 1500 isochromatic images has been assembled. A subspecies of a neural network, namely a convolutional neural network, has been applied as a machine learning algorithm. Different combination of models and optimizers have been employed. The application of downhole sensors for continuous monitoring of alterations in the rock mass stress state and the integration of this data into a digital field model based on Internet of Things technologies has been proposed.

Real-Time Underreamer Vibration Predicting, Monitoring, and Decision-Making Using Hybrid Modeling and a Process Digital Twin

Summary In hole enlargement while drilling (HEWD) operations, underreamers are used extensively to enlarge the pilot hole. Reamer wipeout failure can cause additional bottomhole assembly (BHA) trips, which can cost operators millions of dollars. Excessive reamer shock and vibration are leading causes of reamer wipeout; therefore, careful monitoring of reamer vibration is important in mitigating such a risk. Currently, downhole vibration sensors and drilling dynamics simulations (DDSs) are used to comprehend and reduce downhole vibration, but vibration sensors cannot be placed exactly at the reamer to monitor the vibrations in real time. DDSs are difficult to calibrate and are computationally expensive for use in real time; therefore, the real-time reamer vibration status is typically unknown during drilling operations. A process digital twin using a hybrid modeling approach is proposed and tested to address the vibration issue. Large amounts of field data are used in advanced DDSs to calibrate the HEWD runs. For each HEWD section, calibrated DDSs are performed to comprehend the downhole vibration at the reamer and downhole vibration sensors. A surrogate regression model between reamer vibration and sensor vibration is built using machine learning. This surrogate model is implemented in a drilling monitoring software platform as a process digital twin. During drilling, the surrogate model uses downhole measurement while drilling (MWD) data as inputs to predict reamer vibration. Wipeout risk levels are calculated and sent to the operators for real-time decision-making to reduce the possibility of reamer wipeout. Large volumes of reamer field data, including field recorded vibration and reamer dull conditions were used to validate the digital twin workflow. Then, the process digital twin was implemented and tested in two reamer runs in the Gulf of Mexico. A downhole high-frequency sensor was placed 8 ft above the reamer cutting structure in one field run, and the recorded sensor vibration data and corresponding reamer dull conditions showed a very good match with the real-time digital twin predictions in a low-vibration scenario. Cases in high vibration are needed to fully validate the feasibility and accuracy of the digital twin. State-of-the-art downhole sensors, DDS packages, large amounts of field data, and a hybrid approach are the solutions to building, calibrating, and field testing the reamer digital twin to ensure its effectiveness and accuracy. Such a hybrid modeling approach can not only be applied to reamers but also to other critical BHA components.