Downhole Parameters Research Articles

A Real‐time Lithological Identification Method based on SMOTE‐Tomek and ICSA Optimization

AbstractIn petroleum engineering, real‐time lithology identification is very important for reservoir evaluation, drilling decisions and petroleum geological exploration. A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper. This method can effectively utilize downhole parameters collected in realtime during drilling, to identify lithology in real‐time and provide a reference for optimization of drilling parameters. Given the imbalance of lithology samples, the synthetic minority over‐sampling technique (SMOTE) and Tomek link were used to balance the sample number of five lithologies. Meanwhile, this paper introduces Tent map, random opposition‐based learning and dynamic perceived probability to the original crow search algorithm (CSA), and establishes an improved crow search algorithm (ICSA). In this paper, ICSA is used to optimize the hyperparameter combination of random forest (RF), extremely random trees (ET), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) models. In addition, this study combines the recognition advantages of the four models. The accuracy of lithology identification by the weighted average probability model reaches 0.877. The study of this paper realizes high‐precision real‐time lithology identification method, which can provide lithology reference for the drilling process.

Analysis and development of a mathematical model of the acoustic method for monitoring downhole parameters of the drilling process

Analysis and development of a mathematical model of the acoustic method for monitoring downhole parameters of the drilling process

Daqing Oilfield Cable Controlled Intelligent Separate Injection Technology Process Optimization

The cable controlled intelligent separate injection process realizes the continuous monitoring and real-time measurement and adjustment of downhole parameters, greatly improves the measurement and adjustment efficiency, provides sufficient data for reservoir analysis, and helps to tap the potential of remaining oil in the later stage of ultra-high water cut. However, due to the multi-disciplinary application of downhole tools and complex process, it is found in the previous field test that The number of wells with more than two years of operation of cable controlled intelligent separate injection wells accounts for only 51.9%, which can not meet the needs of large-scale application. Through the fault analysis of the early test well, the key factors affecting the success rate of well completion are found. Aiming at the main problems, the flow control valve of the cable controlled intelligent water distributor, the cable sealing connection process and the well flushing valve structure through the cable packer are optimized and improved. The flow control valve of cable controlled intelligent water distributor is opened and closed smoothly under 10MPa pressure difference, and the leakage is less than 1m³/d; The connection between the cable and the cable controlled intelligent water distributor is sealed reliably, and the cable can pass through the upper and lower joints of the cable packer smoothly; Since the optimization of process technology until January 2023, 223 wells have been tested on site, and the number of wells with more than two years of operation has increased from 56.1% to 91.9%. The stability of the optimized cable controlled intelligent separate injection process has been improved, which lays a foundation for the large-scale application of this technology in the oilfield.

Prediction of drilling plug operation parameters based on incremental learning and CNN-LSTM

In order to solve the problems of low prediction accuracy and insufficient use of long time series data, and ensure the safety of drilling plug operation, a method for predicting drilling plug parameters based on incremental learning and the CNN-LSTM model is proposed. By making full use of the measured data of the drilling plug operation and using the sliding window processing method to process the data, then the CNN-LSTM deep learning model is established to predict the downhole parameters, and the reliability and generalization of the model are verified. The results show that the prediction accuracy of the machine learning model is the highest when the fixed width k = 4. According to the error results, it is concluded that CNN-LSTM model has the best processing and forecasting ability for drilling plug operation. By analyzing the prediction results of the whole well data, the validation accuracy of CNN-LSTM model reaches 99.5%, indicating that the model has high accuracy and generalization, and the model has fast calculation speed, providing a prediction method with high precision and fast calculation speed for drilling plug operation.

Uncertainty analysis method for the output force vector control on the rib of the orienter in coiled tubing drilling

This work addresses the uncertainty associated with downhole parameters in coiled tubing drilling and proposes a new method for controlling the output force vector on the ribs of the orienter, enabling more precise control of the actual wellbore trajectory. Based on the principles of longitudinal and transverse bending beams, we construct a 3D mechanical model of the Bottom Hole Assembly (BHA) used in coiled tubing drilling. We then analyze the spatial geometry relating the force direction on the orienter's rib and the tool face angle, establishing a comprehensive steering resultant force vector model. Applying the principle of minimum energy, we explore traditional control schemes for the output force on each rib and reveal its intricate variation patterns. We conduct a statistical analysis to investigate the randomness distribution state and sensitivity of the experimental parameters, allowing us to derive the probability density and cumulative probability functions for the two-dimensional random variables linked to the output force on the ribs. Confidence interval models are constructed to predict the output forces with a predetermined level of certainty. Finally, we experimentally verify the proposed uncertainty control method in real-world coiled tubing drilling. Our results show that the randomness of weight on bit (WOB) and the tool face angle of rib 1# are the main factors influencing the uncertainty of the output force control. When the output forces vector on the ribs exceeds the uncertainty confidence interval, the actual wellbore trajectory significantly deviates from the designed one. Conversely, when the output forces vector falls within the confidence interval, the error between the actual and designed wellbore trajectories is significantly smaller. This validates the rationality and effectiveness of the proposed uncertainty analysis method, providing valuable guidance for implementing wellbore trajectory control in coiled tubing drilling.

Downhole parameter prediction method based on multi-layer water injection model and historical data-based model parameter identification

Wireless communication has become a preferred direction for the development of layered water injection tools due to its low cost and high reliability. However, the wireless system relies on the underground battery for power supply，and each communication will consume a significant amount of energy. In order to save energy consumption, the wireless system adopts the intermittent sleep communication mode, with intervals of usually more than one month. During the idle time of communication, the downhole parameters such as pressure and flowrate will change as the pressure and flowrate at the wellhead. Therefore, it is crucial to predict downhole parameters based on the wellhead pressure and flowrate. In this study, a downhole parameter prediction method based on multi-layer water injection model is proposed. A multilayer injection prediction model was established based on the hydraulic analysis of the tubing string, and the model parameters were identified and updated using the historical data uploaded each time. The pressure and flow rate measured at the wellhead were used as inputs to the model, and the recursive relationship between layers in the multilayer model was utilized to predict downhole parameters for each layer. A model parameter optimization method based on time-weighting is proposed in order to address the gradual changes in model parameters during water injection. This method assigns greater weight to more recent historical data, resulting in optimized model parameters. Experimental results show that the proposed method can effectively predict the flowrate and pressure of each layer, with a prediction deviation of less than 5% F.S., which provides technical support for the application and popularization of the wireless layered water injection system.

Research and Test on the Device of Downhole Near-Bit Temperature and Pressure Measurement While Drilling

The accurate acquisition of downhole engineering parameters, such as real-time pressure and temperature measurements, plays a crucial role in mitigating drilling risks and preventing accidents. In this study, we present the design of a real-time data acquisition and transmission system for drilling operations. The system utilizes a near-bit measurement method to simultaneously measure downhole parameters, including mud pressure and temperature. By analyzing the pressure and temperature frequencies obtained from a quartz crystal pressure gauge and compensating for temperature effects, accurate pressure values are obtained. The resistance value of a PT1000 sensor is measured, and a second-order fitting is performed using laboratory scale coefficients to determine the temperature values. The data acquisition system employs an advanced microcontroller as the main control chip, along with an A/D conversion chip. Additionally, signal amplification, data storage modules, data transmission modules, and relevant peripheral circuits are designed. The field tests were conducted in the 4605~4620 m well section of well Qing 2-76 in the Yumen Oilfield. The results demonstrate stable transmission signals and accurate decoding, enabling the real-time monitoring of pressure and temperature. The tests yielded favorable outcomes, providing a tangible means to analyze the actual operating conditions of the downhole drill string.

Optimization of a Turbine Flow Well Logging Tool Based on the Response Surface Method

As the exploration of ultra-deep layers and the development of geothermal resources continue, obtaining more accurate downhole parameters becomes increasingly important. Flow measurement, in particular, is a complex parameter that presents significant challenges. In order to enhance the accuracy of the turbine flow logging tool, this research focuses on optimizing the turbine flow metering structure through a range of methods. A calculation model related to the measurement process is established, and the flow field characteristics in the flowmeter are analyzed using CFD software. The sensitivity of various geometric parameters to the meter coefficient is also analyzed, and the significance of 11 influencing factors is classified and optimized using the Plackett–Burman climbing test design. The Box–Behnken design method is then used to conduct an experimental design for the significant influencing factors, and the results show that the regression model fits the actual situation well. The response surface method is used to optimize the structural parameters, and an orthogonal experimental design is carried out for the selected non-significant influencing parameters to obtain the optimal structure combination. After optimization based on the response surface method, the stability of turbine flow measurement accuracy is improved by 34.5%.

Field Test Research on the Downhole Multiphysics Micro-Measurer Based on the MEMS Microchip

Since pressure while drilling (PWD) has the disadvantages of single-point measurement and high cost of application, a micro-measurer based on MEMS (micro-electromechanical systems) sensor technology, which can measure downhole temperature, pressure, magnetic field, and dynamic signal, has been developed to achieve real-time, efficient, and accurate measurement of multiple parameters in the well. The kernel circuit system is the core of measurement and control, and the shell plays the role in protecting the kernel circuit. The shell of the micro-measurer is made of preferably selected materials with high-temperature and high-pressure resistance, corrosion resistance, small size, and low density, which can adapt to working in drilling fluid for a long time. The micro-measurer uses integrated interfaces on the shell to enable communication between the host computer and measuring machine. Based on the field test, both the functional integrity and data measurement accuracy of the micro-measurer are verified. Through analysis of the measured data, the profile of the downhole temperature field is constructed. The physical phenomena reflected by the measured magnetic field signal and dynamic signal are consistent with the actual working conditions observed in the test. Hence, as a new microchip measuring device, the micro-measurer can better serve the drilling engineering field and provide technical support for real-time measurement of downhole parameters in the future.

Trend and dynamic analysis on temporal drilling data and their data-driven models

In recent years, digitalization and automation with using novel tools, like data analytics, artificial intelligence, and smart sensors have been adopted in the Oil and Gas industry as game-changer solutions to the most demanding problems, like downhole parameters’ estimation, formation classification and incidents detection. Moreover, there are the increasing needs for better interpretation and understanding on data itself and, furthermore, on data-driven models/algorithms, especially to facilitate their applications from various engineering perspectives. Our work presents a novel and automatic way to extract trend information from temporal drilling data streams to assist drillers to easily detect drilling process’s trend and dynamic changes. In addition, the proposed trend analysis method can be applied to machine learning models to evaluate models’ applicability considering their ‘black-box’ properties.

Wireless Monitoring of Downhole Pressure Based on Tubing Transmission

Downhole pressure is an important parameter for evaluating the production status of production wells. A wireless monitoring system was designed for downhole pressure which consists of two parts, i.e., the downhole tool and the wellhead tool. The tubing was used as the information transmission medium between the two parts. The downhole tool includes four pressure sensors, and the measured results will be applied to the tubing in the form of an alternating voltage with a frequency of 2.5 Hz. The amplitude of the alternating voltage output by the downhole tool is related to the magnitude of the downhole pressure. The wellhead tool measures and decodes the signal uploaded through the tubing, and the decoded result will be transmitted to the host computer through the ground cable. The wireless transmission model of downhole pressure in the production well was equivalent to a resistance network. Simulation analysis indicates that the amplitude of the signal received by the wellhead tool is negatively correlated with the downhole depth of the downhole tool and positively correlated with the formation resistivity. The designed wireless monitoring system for downhole pressure was tested in gas-producing wells in southwestern China. The test results verified the stability and reliability of the system for downhole pressure monitoring. It also confirmed that the strength of the received signal at the wellhead is negatively correlated with the downhole depth of the downhole tool for the first time and is not affected by the structure of the wellbore. The wireless monitoring scheme of downhole pressure introduced in this paper can be extended and applied to the monitoring of other downhole parameters such as temperature.

Case Studies and Operation Features of Long Horizontal Wells in Bazhenov Formation

Summary In this study, unique field data analysis and modeling of operating wells with an extended horizontal wellbore (HW) and multistage hydraulic fracturing (MHF) in the Bazhenov formation were conducted. Moreover, a large amount of long horizontal well data obtained from the Bazhenov formation field was used. Wells with extended HW drilling and MHF are necessary for commercial oil production in the Bazhenov formation. Problems can occur in such wells when operating in the flowing mode and using an artificial lift at low flow rates. This study aimed to describe the field experiences of low-rate wells with extended HWs and MHF and the uniqueness of well operations and complexities. It was also focused on modeling various operation modes of such wells using specialized software and accordingly selecting the optimal downhole parameters and analyzing the sensitivity of fluid properties and well parameters to the well flow. The flow rates in wells with extended HW and MHF decrease in the first year by 70–80% when oil is produced from ultralow-permeability formations. Drainage occurs in a nonstationary mode in the entire life of a well, leading to complexities in operation. A comprehensive analysis of field data [downhole and wellhead pressure gauges, electric submersible pump (ESP) operation parameters, and phases’ flow rate measurements] and fluid sample laboratory studies was conducted to identify the difficulties in various operating modes. For an accurate description of the physical processes, various approaches were used for the numerical simulation of multiphase flows in a wellbore, considering the change in the inflow from the reservoir. The complexities that may arise during the operation of wells were demonstrated by analyzing the field data and the numerical simulation results. The formation of a slug flow in low flow rates in a wellbore was caused by a rapid decline in the production rate, a decrease in the water cut, and an increase in the gas/oil ratio (GOR) over time. Based on the results, proppant particles can be carried into the HW and thereby reduce the effective section of the well in case of high drawdowns in the initial period of well operation. Consequently, the pressure drops along the wellbore increased, and the drawdown on the formation decreased. Other difficulties were determined to be associated with the consequences and technologies of hydraulic fracturing (HF). These effects were shown based on the field data and the numerical simulation results of the flow processes in wells. In addition, corrective measures were established to address various complexities, and the applications of these recommendations in the field were conducted.

Leveraging Coiled Tubing Conveyance in an Integrated and Rigless Abandonment and Perforation Workflow

Summary In Ecuador, shutoff of an underperforming interval through plug and abandonment (P&A) and perforation of a new interval are traditionally completed with a combination of wireline (WL) and tubing conveyance. An alternative method using enhanced coiled tubing (CT) is presented here; it enables a rigless and efficient workflow that leverages real-time downhole data for on-the-fly optimization. The new workflow relies on CT-conveyed technologies without requiring any additional conveyance methods. CT delivered four different services to start the abandonment by anchoring a 7-in. cast-iron bridge plug (CIBP), complete the abandonment with a low-viscosity cement plug, simulate wellbore dynamics during nitrogen pumping to generate the required underbalanced conditions for perforating, and perforate with a 40-ft ballistic payload of 4 1/2-in. guns. Coupled with real-time downhole telemetry, the enhanced CT workflow provided critical downhole conditions, including fluid levels, accurate depth placement and control, bridge plug setting confirmation, underbalanced conditions, perforating head activation and detonation, and postperforation inflow monitoring. Compared with traditional methods, the enhanced CT workflow introduces several benefits toward completing P&A of old intervals and perforation of new ones. These benefits include enabling a rigless workover (WO) intervention, eliminating the need and cost of a WO rig, reducing operational duration by 13%, and potentially reducing asset footprint and field crews by 95 and 70%, respectively. Elimination of a WO rig reduces environmental impact and the number of personnel on location (i.e., risk). The workflow also extends the reach and efficiency of the service in horizontal wells, enables underbalanced perforation, and delivers actionable real-time downhole data. These data elevate traditional P&A workflows and create a step change in efficiency. First, they allow tracking key downhole parameters that help guarantee a reliable operation of each of the tools and services. Second, they provide insights into the actual downhole conditions throughout the intervention to enable the operator and the field crews to make on-the-fly decisions to deliver a safe and optimal service. Those decisions may include fine-tuning the prescribed treatment or extending the scope of the intervention by leveraging the CT’s pump-through capabilities to maximize well performance and meet, or exceed, the operator’s objectives. The innovative combination of real-time telemetry with abandonment and perforating technologies proved a step change in operational efficiency and range, fueled by the quantity and quality of data recorded during the operation. This case study also marks the first documented perforation with 4 1/2-in. guns with fiber-optic real-time downhole telemetry. Furthermore, the integrated, rigless solution provides operators with an opportunity to extend their WO activity pipeline and free up their WO fleet for other activities.

Study of Downhole Lateral Force Measurement Modelling and Devices in Petroleum Exploration

Measurement while drilling (MWD) technology is important for obtaining downhole parameters. As an essential parameter in drilling engineering, lateral force provides a powerful reference basis for judging the downhole drilling direction. Although stress measurement technology has matured, research on downhole lateral forces, especially near-bit lateral forces during the drilling process in petroleum exploration, is lacking. Based on the force analysis of a short measurement circuit, the lateral force measurement value of a drill bit and centralizer was converted to a drill string’s radial-bending-force measurement. Two perpendicular lateral force components were measured using strain gauge technology, a lateral force measurement theory model was established, and a set of MWD systems was designed according to the model. The systems’ function was verified through laboratory and field tests, and the field-test data were successfully obtained in the field application. The test results showed that the tested MWD system had acceptable accuracy, stability, and reliability and had the application potential to measure lateral force in the drilling industry. This article provides a new idea to study lateral force while drilling, which is of great significance to oil-drilling exploration and development.

The role of overbalance pressure on mud induced alteration of sandstone rock pore system

Overbalance pressure is a very critical parameter in drilling operations. It has a great impact on formation damage, depending on other downhole parameters such as temperature, time, type and composition of mud, and rock mineralogical content. The objective of this study is to determine the degree of the impact of overbalance pressure on mud–rock interaction and the resultant effects on the rock pore system. This research presents an experimental study for the interaction of a Berea Buff sandstone and barite water-based under different overbalance pressure (300, 700, and 1000 psi) under the same temperature and interaction time. The experiments involved the use of the scanning electron microscope and nuclear magnetic resonance relaxation measurements to monitor changes in the pore system of the rock samples. A modified filtration cell was used to accommodate the rock samples and mud at different overbalance pressures. The obtained results showed that the filtration properties, rock flow characteristics (rock permeability, pore throat radius, and pore system scale type) are all affected by increasing the overbalance pressure. The filtration properties increased in terms of mud cake thickness and filtrate volume by 111% and 36% respectively when the overbalance pressure was increased from 300 to 1000 psi. The total rock porosity showed a decrease from 21.6% (pre-mud interaction) to 17.6, 15.2, and 14.2% under 300, 700, and 1000 psi, respectively. The rock permeability decreased by 75% under 1000 psi overbalance pressure while pore throat radius decreased by 45%. However, the rock pore type remains on the same scale (Macro) after interaction with the mud. Statistical analysis showed that the rock porosity and permeability decreased with the overbalance pressure increase through a polynomial relationship with a high determination coefficient of 0.99. Analysis of the internal pore system by the scanning electron microscope showed that the formation damage is mainly attributed to the precipitations of mud solids as overbalance pressure is increased.

Inversion-based model for quantitative interpretation by a dual-measurement points in managed pressure drilling

Deep wells control safety problems have always been the focus of attention due to the influence of complicated geological conditions, such as high-temperature and high-pressure (HTHP). Considering the mathematical model is only approximation of reality, thus the continuous transmission of real-time measurements is more popular, which can be used to identify the undetermined downhole parameters and improve accuracy of the model predictions. In the current study, we use two pressure while drilling tools to monitor downhole parameters in real time. Firstly, combined with the unscented-Kalman-filter (UKF) algorithm, a well hydraulics forward model with density factor and friction factor based managed-pressure-drilling (MPD) system is developed to quantitatively analyze the actual transient annular pressure. Secondly, a new multi-phase flow inversion model is established by determining the inversion parameters as gas kick rate and the position of the gas kick, which is used to track the values of pressure and outlet flow rate in real-time. The results of the case indicate that the inversion accuracy of the inversion parameters increases with the distance between dual-measurement points, and the reasonable value is selected as 30 m. Additionally, the proposed model is successfully validated using synthetic data and experimental data, where the maximum errors in predicting the position of the gas kick are only 3.1% and 5.3% when tuning the model stably tends towards the true value, respectively. The proposed model achieves accurate quantitative interpretation and analysis of downhole gas kick conditions, further clarifies the previously unknown key parameters of downhole gas kick, and has important significance for the realization of safe and efficient drilling.

Downhole data correction for data-driven rate of penetration prediction modeling

In recent years, machine learning has been adopted in the Oil and Gas industry as a promising technology for solutions to the most demanding problems like downhole parameters estimations and incidents detection. A big amount of available data makes this technology an attractive option for solving a wide variety of drilling problems, as well as a reliable candidate for performing big-data analysis and interpretation. Nevertheless, this approach may cause, in some cases, that petroleum engineering concepts are disregarded in favor of more data-intensive approaches. This study aims to evaluate the impact of drilling data measurement correction on data-driven model performance. In our study, besides using the standard data processing technologies, like gap filling, outlier removal, noise reduction etc., the physics-based drilling models are also implemented for data quality improvement and data correction in consideration of the measurement physics, rarely mentioned in most of publications. In our case study, recurrent neural networks (RNN) that are able to capture temporal natures of a signal are employed for the rate of penetration (ROP) estimation with an adjustable predictive window. The results show that the RNN model produces the best results when using the drilling data recovered through analytical methods. Moreover, the comprehensive data-driven model evaluation and engineering interpretation are conducted to facilitate better understanding of the data-driven models and their applications.

Evolution of static and kinetic friction in a horizontal well using an adaptive model-based observer: Field validation

Evolution of static and kinetic friction coefficients across the depth of a horizontal wellbore and field validation against the downhole parameters is presented in this paper. It has been observed in the literature that stick–slip oscillations exist in drill strings even with bit off-bottom and persist with the bit on bottom. In the past, various lumped mass models have failed to explain this phenomenon, as they have only considered bit–rock interaction to be the main reason for the stick–slip oscillations. The model considered in this study was used previously to estimate the friction factors at particular connections with bit off-bottom. For effective estimation of the downhole parameters, the model employs an adaptive soft sensor, robust to capture the disturbances occurring at the downhole. Using only the topside measurements (i.e., surface measurements), downhole RPM is estimated along with the evolution of friction coefficients (static (μs) and kinetic (μk)) using the soft sensor. For obtaining the friction factors profile, those values of μs and μk that converge and remain stable for a specified time are considered. The model is first validated against the downhole RPM data obtained for a well in North America. It was observed that the estimates of the downhole RPM obtained using the model are in close agreement with that of the actual data. Next, the model was employed to obtain reasonable downhole RPM estimates for another well in North America where the downhole data is unavailable.

PROBLEM PREDICTION DURING TRIP IN AND TRIP OUT PROCEDURES WITH ARTIFICIAL NEURAL NETWORKS

In recent times, the adaptation of artificial intelligence (AI) technologies has been spread in the petroleum industry. Such methods as Artificial Neural Networks (ANN), Fuzzy Logic, or Evolutionary Computing have the potential to improve the currently applied methods in every sector of the industry. They provide an advanced encroachment of the complex physics of downhole parameters, which directly add to their modeling ability compared to the traditional empirical and analytical methods. In this study, the development of a feed-forward neural network is presented. The purpose of the development is to predict the possible problems in case of a drilling operation, during running in and pulling out of the hole (RIH & POOH), based on the data acquired during the drilling of the hole.

Research on Downhole Plunger Position and Pressure Temperature Detection of Plunger Gas Lift System

Plunger gas lift utilizes the energy of gas production in the formation. By periodically switching on and off the gas well, the plunger moves up and down in the tubing to discharge the bottom hole fluid. The purpose is to reduce the back pressure on the production layer and increase the gas production of the formation. Power, prolong the production life of the gas well. The plunger gas lift detection is the core part of the plunger gas lift system. Adjusting the working status of the downhole plunger through the detection of downhole plunger position, temperature and pressure has important engineering value for improving the oil recovery efficiency of the gas lift process. In order to promote the development of downhole detection systems for gas lift wells in my country and overcome the difficulties of late start and lack of experience, this article focuses on the development status of the detection of plunger gas lift systems.the paper briefly introduce the composition of the plunger gas lift system, analyze the problems of the plunger arrival position detection and temperature and pressure detection in the gas lift detection system, and then analyze the current status of the plunger position monitoring of the plunger gas lift downhole. A review is made, followed by a review of the current status of domestic temperature and pressure monitoring systems for plunger gas lift wells. The review found that the detection of plunger position in my country is still at a relatively weak stage, and direct measurement cannot be achieved; however, there have been breakthroughs in the detection of temperature and pressure, especially in the fields of pressure and temperature detection, storage and wireless transmission. breakthrough. The analysis believes that the detection of downhole parameters of plunger gas lift should be developed in the direction of the integration of well digital control and data acquisition system and digital measure adjustment.