Early Kick Detection Research Articles

Early Kick Detection by Using the Electrical Conductivity and Mud Density in Nahr Umr Oil Field

Early Kick Detection by Using the Electrical Conductivity and Mud Density in Nahr Umr Oil Field

Statistical analysis of past kicks and blowouts occurred in a Middle Eastern oilfield

In 2017, blowouts and then explosions occurred in a Middle Eastern oilfield. A root cause of the incident is lack of study and investigation of the past kicks and blowouts data. Therefore, as a pioneer work in the region, data gathering and analysis of past kicks and blowouts were made in the studied oilfield to learn lessons and find gaps. Out of the 149 drilled wells, a total of 117 kicks and three (3) blowouts occurred. In this work, a list of drilling parameters to be considered in data gathering and analysis were suggested as a guideline for future works elsewhere. The statistical analysis not only showed all the three exploration wells kicked which is not a surprise, but it also showed that 39 out of 146 (26.71%) also experienced kicks during reservoir drilling. The large number of kicks in development wells proved that possibility of kick occurrence in development wells is not low. In exploration wells, the predominant kick causes were gas-cut mud and insufficient mud weight which indicates the necessity of using pressure while drilling in addition to drilling rate control systems in exploration wells. However, in development wells, lost circulation was the predominant kick cause indicating the necessity of using low-weight drilling fluids and managed pressure drilling systems. The direct role of human error exists at least in 60% of kicks occurred in this field, which shows the great importance of improved drilling personnel training. Although only 3.45% kicks in development wells occurred due to improper hole fill-up during tripping, this cause should not only be deemed trivial, but it should also be taken seriously as being the cause of the blowout. The 2.56% possibility of kick conversion to blowouts and 67% risk of blowout conversion to explosion emphasize the necessity of maintaining primary well control and using efficient and early kick detection systems. Bullhead was the more commonly used method than standard well control methods; as this kill method may not always be safe, its application should be revised.

An enhanced data-driven framework for early kick detection based on imbalanced multivariate time series classification

An enhanced data-driven framework for early kick detection based on imbalanced multivariate time series classification

A hierarchical early kick detection method using a cascaded GRU network

The lagged response of some monitoring parameters leads to alarm delays in a multi-parameter kick warning system. In order to improve the timeliness of kick warning, a hierarchical early kick detection method using a cascaded gated recurrent unit (GRU) network is proposed. In this method, the GRU is used as the basic unit to detect abnormal parameter changes, a graded kick warning model is constructed, in which a risk assessment of low, moderate, high likelihood is took depending on the number of parameters that have been abnormal at different moments. The cascaded network monitors parameters of different sensitivities in groups, and obtaining better data fitting effect by reducing the number of parameters detected by each level of the network, thus achieving a more accurate classification of the kick likelihood. In addition, a new parameter priority indicator, that measures the change rate and reaction time of the parameter using the variation of the parameter, is proposed for parameter responsiveness ranking. The test results using the field data from 22 wells show that the proposed method achieves the correct grading of low risk to high risk. Compared with the conventional GRU model, the kick recognition-accuracy is improved by 5.88%, while the alarm time is 1.5 min earlier on average.

An intelligent model for early kick detection based on cost-sensitive learning

Kick detection is crucial for ensuring process safety of drilling operation. Detection of a kick at early stage leaves more time for the drilling crew to take necessary actions. In this work, a novel intelligent model is proposed for early kick detection, which incorporates feature transformation, cost-sensitive dataset construction, and ensemble learning. It applies 7 wellhead feature parameters as input. The model is trained and tested with the field data of a shale gas reservoir in Sichuan. The model performances under different data dimensions and misclassification costs are evaluated. It is found that when the data dimension is 6 and the misclassification cost is 3, the model has the best classification ability (Total Cost=0.9, Accuracy=0.998, Recall=0.990, Precision=0.986). The low false alarm rate helps to minimize wastage of drilling time. The ablation experiment and the comparison with conventional sampling methods unanimously prove the superiority of the proposed model. Datasets with various sizes and imbalance ratios are tested and the model shows satisfactory accuracy. The formula of the optimal misclassification cost is derived for the instruction of field application. The early kick detection performance of the proposed model is better than the existed methods.

Automated well control: A step change in safety

In the oil and gas industry, the operation of drilling is a process which has been manually controlled. Well control is a safety critical function, and it is used to enable safe management of pressures and fluids encountered whilst drilling. Losing well control resulting in an uncontrolled flow of reservoir fluids to surface and subsequent blowout, or fuel fed fire, can occur. The process of controlling this major accident hazard has also been manually controlled and is therefore subject to significant human factors' issues. Each year multiple blowouts and several fatality events happen due to a loss of well control. Traditionally, well control has been completely reliant on a human to identify and react to an influx. However, the human condition means the driller can be distracted, or suddenly influenced by extraneous factors. Overreliance on humans in well control poses a danger to the safety in well operations, because of the inherent and constant exposure to human factors risk. Tasks that require sustained periods of cognitive awareness and reaction can be best performed by an automated process. Other industries have successfully implemented automation to reduce the risks and produce more consistent and efficient outcomes; however, automation of well control has not yet been fully applied. The oil and gas industry can improve from a personnel safety, environmental and reputational perspective. As in other industries, automating the function of well control represents a significant improvement in process safety for drilling operations. With the rapid advancement of technologies associated with simulators and cyber-rigs over the past 20 years, several new technologies have emerged. One of them is a technology which enables automated well control whilst in drilling mode. The Automated Well Control system has been designed to fully automate influx detection and shut-in sequences. Once the system detects the influx, it performs a series of automated operations by taking control of the drilling rig equipment. The drill string is spaced out, top drive and mud pumps are stopped, and the BOP is closed. The system is currently designed for the drilling phase, which also covers initial tripping off bottom and back-reaming, and has been substantially tested against drilling simulators with real drillers. Additionally, a full field trials has been conducted using a conventional land rig which demonstrated the effectiveness of the standard system, proving up the functionality under different operational requirements. A technology qualification exercise was conducted, and the system has received a Technology Qualification Certificate for cyber and traditional rigs. To date, there are around 100 potential modules that can be developed using the same technology to cover every aspect of well construction and decommissioning operations. Existing systems in use on rigs, such as Managed Pressure Drilling (MPD) and Early Kick Detection Systems (EKDS), can also benefit from linking directly with the Automated Well Control system to facilitate a fast and effective re-establishment of the secondary barrier. The Automated Well Control and a MPD systems have recently been combined to create the first integrated package to deliver both pressure control and well control in an efficient and less error prone manner. A full rig trial was successfully performed to demonstrate and verify the integration and functionality of both systems. The Automated Well Control technology referred to above has been patented by the UK Patent Office, which recognises its ability to detect the presence of a fluid influx condition in a wellbore, make a decision against criteria to shut-in, and then automatically initiate a well control protocol that results in the safe shut-in of the well. The paper aims to describe the Automated Well Control technology and explain why the system was designed and how it works, highlighting the benefits of automating this safety critical process.

An analytical study on early kick detection and well control considerations for casing while drilling technology

Casing while drilling (CwD) technology is designed to reduce drilling time and expenses by improving the wellbore stability, fracture gradient, and formation damage while reducing the exposure time. However, for the purpose well control, the wellbore geometry and volumes differ from those obtained via a conventional drilling technique, thereby requiring a different approach. This study discusses well control principles for CwD operations. It presents a simplified method for evaluating the maximum kick tolerance and allowable well shut-in time for both conventional and CwD techniques using a mathematical model. Preliminary results revealed that the use of CwD leads to an annulus pressure loss three times higher than that observed in the conventional drilling. In addition, the kick tolerance is reduced by 50% and the maximum allowable well shut-in time is reduced by 65%, making an early kick detection system necessary.

Development of a Dual-Measured-Points Early Gas Kick Detection Method based on the pressure responses of two PWD tools

Early gas kick detection is an important measure to ensure well control safety. In this paper, firstly, a new two-phase flow model was developed to accurately describe the transient annular pressure responses at the PWD tool during the gas influx. Secondly, a Dual-Measured-Points Early Gas Kick Detection Method was proposed based on the consistency and discrepancy between the pressure responses of two PWD tools. The results of case research indicated that the annular pressure increased and changed periodically with time in the initial stage of gas kick and then declined approximately linearly after the gas-liquid interface reached the PWD tool. Additionally, the proposed gas kick detection method could significantly reduce the chances of the gas kick misdiagnosis and improve the reliability of the gas kick detection. Moreover, the gas kick confirmation time (GKCT) obtained by the proposed method was 2–7 min, and the corresponding gas kick confirmation volume (GKCV) was 0.3–0.5 m3. The GKCT diminished with the increase in the formation permeability and the bottomhole pressure difference and with the decrease in the borehole size and the well depth.

A new methodology for kick detection during petroleum drilling using long short-term memory recurrent neural network

Kick is a downhole phenomenon which can lead to blowout, and so early detection is important. In addition to early detection, the need to prevent false alarm is also useful in order to minimize wastage of operation time. A major challenge in ensuring early detection is that it increases the chances of false alarm. While several data-driven approaches have been used in the past, there is also ongoing research on the use of derived indicators such as d-exponent for kick detection. This article presents a data-driven approach which uses d-exponent and standpipe pressure for kick detection. The data-driven approach presented in this article serves as a complementary methodology to other stand-alone kick detection equations, and uses d-exponent and standpipe pressure as inputs.This paper proposes a methodology which uses the long short-term memory recurrent neural network (LSTM-RNN) to capture temporal relationships between time series data comprising of d-exponent data and standpipe pressure data with the aim of increasing the chances of achieving early kick detection without false alarm. The methodology involves obtaining the slope trend of the d-exponent data and the peak reduction in the standpipe pressure data for training the LSTM-RNN for kick detection. Field data is used for training and testing. Early detection was achieved without false alarm.

Kick detection and remedial action in managed pressure drilling: a review

Increasing the global demand for natural resources directs the oil industries to explore in geologically challenging structures and offshore reserves. Oil industries are always searching for innovative drilling technologies to optimize field development process in a complex structure. Managed pressure drilling (MPD) is now becoming an attractive alternative to the traditional overbalance drilling in complex formation. MPD offered substantial benefits in terms of project economics and reduced non-productive time (NPT). These benefits are substantial in the offshore structure, where any downtime significantly impacts the project cost. MPD is designed to avoid continuous formation influx into the wellbore, and any incidental fluid is contained with a specific predetermined process. MPD used some specialized tools and techniques to enhance traditional kick detection capabilities and circulate formation influx while keeping NPT at the minimum level. Early kick detection is a primary concern for the drilling industry to ensure the safety of the drilling rig, crews, and environmental protection. This research focused on a systematic review of kick detection and mitigation in MPD operation. A review of recent advancements in MPD, various early kick detection methods, comparative study of different kick indicators with their significance, different gas kick models, and risk analysis are analyzed systemically. Several control methods in the MPD operation are summarized. A systematic comparison of different gas kick circulation methods in conventional drilling and MPD is presented in this study. Also, different alternative responses to conventional kick circulation methods are summarized. This work critically analyzed different kick responses of circulating and non-circulating methods, e.g. shut-in, modified pump shut down, increasing in casing pressure and stepwise increase in pump rate. However, all circulation methods are elementary, and no kick circulation method is universally applicable to all drilling operations. Finally, this review emphasized some recent progress and challenges in kick detection on managed pressure drilling.

Kick Detection Using Downhole Accelerometer Data

Abstract The high-frequency downhole vibration data include a greater amount of hidden information than the low-frequency surface data. This paper proposes the monitoring of high-frequency acceleration data for early kick detection. The trend of accelerator sensor values is monitored, rather than processed. When the gas, fluid, or oil kick occurs, the fluid influx reduces the viscosity of the fluid in annulus, which causes the degradation of the damping factor. The sensor installed on the drillpipe detects the velocity/acceleration change that results in the damping factor change. This approach includes an analytical model to calculate the effect of the damping ratio on the acceleration calculations. The fluid influx and migration in the wellbore strongly affect the damping factor. The paper presents a method of deconvoluting the sensor values that uses a combination of minimum entropy deconvolution and Teager-Kaiser energy operator to remove the noise, unwanted sensor values, and likelihood of false prediction. It is then proposed to calculate instantaneous jerk and jerk intensity at each depth. The trend of the final intrinsic mode functions (IMF) at each depth is continuously monitored to predict the formation influx, if any. A novel concept of monitoring the incremental IMF and IMF energy at each depth is introduced. This technique is shown to reveal a wealth of information and simplifies the process of monitoring and analyzing the vast amount of available data. The methodology developed is applied to extract the essential information from high-frequency vibration data to make real-time data monitoring straightforward, reliable, and fast.

Supervised data-driven approach to early kick detection during drilling operation

The margin between pore pressure and fracture gradient in new offshore discoveries continues to get narrower. This poses greater risks and higher cost of ensuring safety of lives, facilities and the environment. The 2010 Macondo blowout has fueled increased interests in monitoring downhole parameter for early kick detection. Early detection of kick is important part of the process safety. It provides opportunity to activate safety measures. This work investigates the simplest supervised learning-based kick detection system to ensure higher reliability using experimental data. It combines an Artificial Neural Network (ANN), binary classifier and downhole monitoring of drilling flow parameters to build data-driven kick detection models. Data was generated from experiment that monitors downhole parameters when kick occurs. Recorded parameters such as mud flow-out rate, conductivity, density and downhole pressure are combined to build the model capable of detecting kick events. Abnormal pressure and flow regimes in the wellbore provide early warnings and was shown to be more significant parameters than others, however relying on them alone could increase susceptibility to a false alarm. The model has been tested on data from two different laboratory scale drilling simulator.

Data-driven Bayesian network model for early kick detection in industrial drilling process

Kick, or hydrocarbon influx, is one of the significant challenges during the drilling operation. A kick happens when the formation pressure exceeds the hydrostatic pressure of mud weight. Detection of a kick at an early stage spares more time to take necessary actions to prevent its growth and mitigate the potential well blowout. There are varieties of methods applied for early kick detection. The conventional method entails monitoring surface parameters which leads to delay in the detection. Some recent works show the ability to employ monitoring of downhole parameters to realize early kick detection. Data-driven Bayesian Network (BN) has shown to solve problems in complex systems where the knowledge about the system is not adequate to apply a model-based method. Data-driven BN creates a model based on historical data, which is usually available, unlike expensive, and often insufficient, expert knowledge. Using the data obtained in a laboratory-scale experiment, this paper presents the application of data-driven BN model in using downhole parameters to early kick detection.

Investigation of methane/drilling mud phase behavior and its influence to hydrocarbon drilling activity

AbstractKnowledge of methane dissolution behavior in drilling mud is of major importance to early kick detection and well control operation in oil and gas drilling industry. In this work, methane solubility experiments in water‐based and oil‐based muds were conducted over wide ranges of temperature and pressure conditions. The influences of mud compositions and methane phases were investigated accordingly. It is found that methane solubility in water‐based mud is minimal and can be ignored. The data in oil‐based mud were analyzed using different equation of state and mixing rules. Our results show that the combination of our novel binary interaction parameter equations, Peng‐Robinson equation, and van der Waals2 mixing rule provides a very good representation of our data as well as those available in the literature. With the proposed model, the annulus methane solubility profile of a deepwater well in South China Sea was predicted, which provides a better understanding of the annulus flow behavior and further improve the safety of drilling operation.

Time series data analysis for automatic flow influx detection during drilling

Automatic and early detection of flow influx during drilling is important for improving well-control safety. In this paper, a new method that can automatically analyze real-time drilling data and detect the flow influx event is presented. The new method combines the physics-based dimension reduction and time-series data mining approaches. Two kick indicators are defined, representing the drilling parameter group (DPG) and flow parameter group (FPG), respectively. Additionally, two real-time trend-analysis methods, the divergence of moving average (DMA), and the divergence of moving slope average (DMSA) are applied to quantify trend evolutions of the two indicators. The kick event is identified based on the anomalous trends held by the two kick indicators. A final kick-risk index (KRI) is calculated in real time to indicate the probability of kick events and to trigger the alarm. The method is tested against four offshore kick events. With KRI threshold setting as 0.8, the average detection time is 64% less than the reported detection time. The application of DPG kick indicator allows the early kick detection without additional downhole sensors or costly flow meters.

Experimental investigation of gas kick effects on dynamic drilling parameters

Blowout incidents not only lead to fatalities but also cause loss of assets, expensive clean-up, costly incident investigations and reports, and negative impact on the environment. The 2010 Macondo blowout accident in the Gulf of Mexico was an eye-opener for many oil and gas operators and oilfield service companies; thus, making early kick detection technology research one of the top industry agendas. However, only limited progress has been made in detection technologies that focus on downhole parameters due to the complexity of offshore drilling operations that is increasingly shifting towards the deepwater. Therefore, the current paper experimentally explores downhole drilling parameters for kick indication during drilling. The study utilizes a fully instrumented laboratory scale drilling rig coupled with an air injection and surface monitoring systems. This study observed a sudden jump in bottomhole pressure, increased volume of the return fluid, decreased density of the return fluid, reduced rate of penetration, and increased rotary speed as indicators of kick. The most significant new finding, which is also validated with field reports, is the dampening effects of the drilling vibrations due to kick. Frequency analysis of the axial bit–rock displacements/vibrations confirms the changes of frequencies due to kick induction during drilling. Coupling this important finding with dynamic drilling models, the response of the drilling system at surface (e.g. standpipe, choke pressures, etc.) indicating this change can be predicted.

A new pattern recognition model for gas kick diagnosis in deepwater drilling

Early kick detection is important to reduce the possibility of well blowouts, which have serious safety and financial implications in deepwater drilling. In this paper, a novel pattern recognition model for kick diagnosis is proposed. In the model, trend detection is used to make good decisions based upon noisy drilling data. The integrated model comprises two parts: (i) a dynamic wellbore flow model, which extracts the kick mode via multiphase flow simulation; and (ii) improved piecewise approximation and similarity measure algorithms. The proposed model successfully diagnoses gas kick faults in real time when it is applied in a field well. It offers significant sensitivity improvements while reducing the false alarm rate caused by ambiguous data, as both kick and non-kick events are accurately extracted and rapidly identified. This model is a new attempt to combat the problem of early kick detection via pattern recognition.

A Numerical and Experimental Study of Kick Dynamics at Downhole

The early detection of a kick and mitigation with appropriate well control actions can minimize the risk of a blowout. This paper proposes a downhole monitoring system, and presents a dynamic numerical simulation of a compressible two-phase flow to study the kick dynamics at downhole during drilling operation. This approach enables early kick detection and could lead to the development of potential blowout prevention strategies. A pressure cell that mimics a scaled-down version of a downhole is used to study the dynamics of a compressible two-phase flow. The setup is simulated under boundary conditions that resemble realistic scenarios; special attention is given to the transient period after injecting the influx. The main parameters studied include pressure gradient, raising speed of a gas kick, and volumetric behavior of the gas kick with respect to time. Simulation results exhibit a sudden increase of pressure while the kick enters and volumetric expansion of gas as it flows upward. This improved understanding helps to develop effective well control and blowout prevention strategies. This study confirms the feasibility and usability of an intelligent drill pipe as a tool to monitor well conditions and develop blowout risk management strategies.

Technology Update: Retrofitting MPD Systems to Deepwater Rigs Aids Drilling, Efficiency, and Process Safety

Technology Update The unique properties of deepwater formations pose significant challenges to the capabilities of conventional drilling rigs. By retrofitting rigs with automated drilling systems, including managed-pressure drilling (MPD) equipment, operators and contractors in global deepwater basins can optimize efficiency and economics while maintaining the level of safety that the industry and regulatory community have mandated for this high-stakes arena. MPD, an adaptive drilling process that actively controls annular pressure throughout the wellbore, is increasingly being integrated into the risers of existing deepwater drilling vessels (Fig. 1). It is the cornerstone of an automated well-control strategy that augments and enhances standard well-control protocols. The technique provides distinct drillability, efficiency, and process safety benefits in deepwater environments. It addresses the higher level of priority that the industry has placed on process safety, which largely involves the ability to accurately and actively monitor and control operations while drilling. Long used in land and shallow-water drilling, MPD-ready rigs are proving their value in deepwater operations by delivering a reliable level of automation and process control when the risk of a well-control incident is highest. Fluctuations in wellbore pressure often create drilling hazards that result in lost circulation, kicks, and other problems that can significantly increase rig time and cost, or lead to catastrophic well-control incidents. An automated MPD system installed onto a deepwater semisubmersible rig or drillship can effectively monitor and maintain the annular hydraulic pressure profile by applying common MPD variants, such as constant bottomhole pressure (CBHP) and pressurized mud cap drilling (PMCD). The ability to continuously monitor various wellbore parameters—particularly those related to downhole pressure—while drilling and making connections also enhances personnel preparedness by serving as an extra set of eyes to supplement those of less-experienced drillers. While expanding the drilling envelope and monitoring wellbore pressure profiles, an MPD system that provides automated advanced flow detection, including early kick detection, also reduces the risks of human intervention by mitigating the possibility of gas in the riser.

Technology Focus: Well Integrity and Well Control

Technology Focus One of the more important aspects of well integrity during drilling operations is early kick detection. When an unintentional flow of the formation fluid into the wellbore occurs during conventional drilling operations, it must be detected promptly and the flow must be stopped, normally by closing the well. The early detection is crucial in minimizing the influx size. When the amount of formation fluid inside the well is large, especially if it is gas, the pressure inside the well will be higher during the subsequent well-control operations. This can lead to an increase in time to control the well or even to a worse situation: the loss of control. Another concern may be the amount of formation fluid to be handled at surface. Deepwater, high-pressure/high-temperature, and slimhole drilling are situations where early kick detection is mandatory. The early kick detection is accomplished with a rig equipped with the appropriate kick-detection sensors and alarms and with a rig crew trained in quickly recognizing a kick and in the shut-in procedures. However, there are situations where early kick detection becomes more problematic—for example, when operating on a floating rig because of its motions, when using non-aqueous drilling fluids because of gas solubility, or during connections. Recently, new technologies and research have been applied or developed to improve the kick-detection systems and to overcome some of the difficulties. To cite just a few examples, Development of automated kick-detection systems (one of the papers summarized here addresses kick detection during connections) Kick detection just above the bit using logging-while-drilling information Kick detection using wired drillstring Research on the effect on kick detection of gas solubility in nonaqueous drilling fluids (mineral oil, paraffin, ester, and olefins) The use of managed-pressure-drilling systems (one of the papers recommended for additional reading comments on the advantage of this technology in reducing the kick size) Recommended additional reading at OnePetro: www.onepetro.org. SPE/IADC 173153 A Barrier-Analysis Approach to Well-Control Techniques by D. Fraser, Argonne National Laboratory, et al. SPE 180047 Impact of New and Ultrahigh-Density Kill Fluids on Challenging Well-Kill Operations by T. Rinde, Acona Flow Technology, et al. SPE 180053 A Numerical Study of Gas-Kick Migration Velocities and Uncertainty by K.K. Fjelde, University of Stavanger, et al.