Understanding Real-Time Job Signatures on Deepwater Cementing Jobs with Dynamic Losses

2021 ◽  
pp. 1-10
Author(s):  
Mohammed Dooply ◽  
Michael Schupbach ◽  
Kenneth Hampshire ◽  
Jose Contreras ◽  
Nicolas Flamant
Keyword(s):  
Real Time ◽  
Flow Rate ◽  
Simulated Data ◽  
Return Rate ◽  
Return Flow ◽  
Job Evaluation ◽  
Wellhead Pressure ◽  
Lost Circulation ◽  
Primary Cementing ◽  
Drilling Rigs

Summary Two of the most important parameters to monitor during a primary cementing job are the flow rate in and return flow rate measurements. To achieve optimum job results of a primary cementing job, measuring annular return rates and comparing them with simulated data in real time will provide a better understanding of job signatures and result in the best possible top of cement (TOC) estimation prior to running any cement evaluation log or making a decision to continue drilling the next section of the well. The return rate job signature along with the wellhead pressure is essential to understanding the behavior and discrepancies between simulated and acquired surface data. Therefore, to assess the risk of job issues, such as unsuspected washout and lost circulation among others, accurate measurements of the return rate are critical. Historically, the cement job evaluation has been limited by the fact that most drilling rigs do not have an accurate flowmeter installed on the annulus return line, and a simple verification of mud tanks volume vs. pumped volume, as reported by drillers or mud loggers, more often than not results in an unreliable assessment of the volume lost downhole, due to the unfamiliarity with the U-tubing effect and lack of data consolidation from the cement unit (flow rate in) and the rig (flow rate in and flow rate out). In this paper, we will review a solution developed to mitigate the lack of a direct flow-rate measurement by computing and displaying the return rate using either a paddle meter measurement or the derivative over time of the volume observed in the rig tanks.

Application of Real-Time Solid Measurement and Return Flow Rate During Coiled Tubing Milling Operations

2021 ◽  
Author(s):  
Kaveh Yekta ◽  
Ray Phung ◽  
Benjamin Stang ◽  
Tyler Woitas
Keyword(s):  
Real Time ◽  
Flow Rate ◽  
Acoustic Noise ◽  
Acoustic Monitoring ◽  
Ultrasonic Flowmeter ◽  
Return Flow ◽  
Monitoring Device ◽  
The Real ◽  
Coiled Tubing ◽  
Milling Operations

Abstract Among the many applications of Coiled Tubing (CT) services, milling plugs and wellbore sand cleanout are two of the major applications. The transport of solids to the surface, as well as monitoring the return rates, are two sources of information which can have a significant impact on the execution of these jobs. Traditionally the flowback crew communicates this information to the CT control cab upon request. However, by utilizing an acoustic monitor and ultrasonic flowmeter, it can reduce the dependence on flowback operators and provide real-time solid measurement and return flow rate. The acoustic monitor is a passive, non-intrusive device that is designed to measure the acoustic noise induced into the pipe wall as solids impact the inside wall of the pipe. The ultrasonic flowmeter is a clamp-on device that is designed with two transducers that serve as both a transmitter and receiver. In order to prove the concept, five stages of trials were planned and executed. In stage one, CT was rigged into a 150m vertical test well. The equipment included CT mast unit, CT pump, choke manifold, and acoustic monitoring device. Several debris piles from previous milling operations were introduced to the test well, and a CT cleanout was performed. The acoustic monitor system measured the amount of solid to surface, and the results were evaluated. Solids retrieved were then compared to the initial debris piles and correlated to the data received by the acoustic monitor. On the 2nd stage, the acoustic monitoring device was utilized in actual milling operation. The 3rd stage was a yard trial of ultrasonic flowmeter using a CT pump and data acquisition system to evaluate the working envelope of this device, followed by a field trial, in stage 4, utilizing the flowmeter in actual milling operations. The final stage of this trial was planned and executed in milling operations on a five wells pad, utilizing the combined applications of acoustic monitoring (solid measurement) and ultrasonic flowmeter (return rate) devices. All five stages contributed to proof of concept for the applications of solid measurement and return flow rate devices. These trials were successfully planned, executed, and evaluated. The acquired data throughout the five stages of these trials were utilized during and post job operations as lessons learned to optimize the process for future applications. The real-time measurement of solids and flow rate monitoring, independent of flowback operators, enables the CT operator to make informed decisions throughout milling and cleanout operations. The real-time streaming of solids to surface and return flow rate enables the operator and service company’s Engineering team to evaluate and optimize the execution of milling and sand cleanout operations.

Hydraulic model of a water cooling system in a chemical plant

1988 ◽  
Vol 53 (4) ◽  
pp. 788-806
Author(s):  
Miloslav Hošťálek ◽  
Jiří Výborný ◽  
František Madron
Keyword(s):  
Flow Rate ◽  
Cooling System ◽  
Cooling Water ◽  
Graph Algorithm ◽  
Automatic Generation ◽  
Hydraulic Calculation ◽  
Return Flow ◽  
Flow Rates ◽  
Pressure Losses ◽  
Controlled Flow

Steady state hydraulic calculation has been described of an extensive pipeline network based on a new graph algorithm for setting up and decomposition of balance equations of the model. The parameters of the model are characteristics of individual sections of the network (pumps, pipes, and heat exchangers with armatures). In case of sections with controlled flow rate (variable characteristic), or sections with measured flow rate, the flow rates are direct inputs. The interactions of the network with the surroundings are accounted for by appropriate sources and sinks of individual nodes. The result of the calculation is the knowledge of all flow rates and pressure losses in the network. Automatic generation of the model equations utilizes an efficient (vector) fixing of the network topology and predominantly logical, not numerical operations based on the graph theory. The calculation proper utilizes a modification of the model by the method of linearization of characteristics, while the properties of the modified set of equations permit further decrease of the requirements on the computer. The described approach is suitable for the solution of practical problems even on lower category personal computers. The calculations are illustrated on an example of a simple network with uncontrolled and controlled flow rates of cooling water while one of the sections of the network is also a gravitational return flow of the cooling water.

scholarly journals Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 20
Author(s):  
Reynaldo Villarreal-González ◽  
Antonio J. Acosta-Hoyos ◽  
Jaime A. Garzon-Ochoa ◽  
Nataly J. Galán-Freyle ◽  
Paola Amar-Sepúlveda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Real Time ◽  
Binary Classification ◽  
Simulated Data ◽  
Classification Model ◽  
Rt Pcr ◽  
Simon Bolivar ◽  
Polymerase Chain ◽  
Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

scholarly journals Dynamic Modelling and Simulation of a Multistage Flash Desalination System

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 522
Author(s):  
Qiu-Yun Huang ◽  
Ai-Peng Jiang ◽  
Han-Yu Zhang ◽  
Jian Wang ◽  
Yu-Dong Xia ◽  
...  
Keyword(s):  
Real Time ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Dynamic Change ◽  
Seawater Temperature ◽  
Dynamic Modelling ◽  
Optimal Operation ◽  
Simultaneous Solution ◽  
Time Optimal

As the leading thermal desalination method, multistage flash (MSF) desalination plays an important role in obtaining freshwater. Its dynamic modeling and dynamic performance prediction are quite important for the optimal control, real-time optimal operation, maintenance, and fault diagnosis of MSF plants. In this study, a detailed mathematical model of the MSF system, based on the first principle and its treatment strategy, was established to obtain transient performance change quickly. Firstly, the whole MSF system was divided into four parts, which are brine heat exchanger, flashing stage room, mixed and split modulate, and physical parameter modulate. Secondly, based on mass, energy, and momentum conservation laws, the dynamic correlation equations were formulated and then put together for a simultaneous solution. Next, with the established model, the performance of a brine-recirculation (BR)-MSF plant with 16-stage flash chambers was simulated and compared for validation. Finally, with the validated model and the simultaneous solution method, dynamic simulation and analysis were carried out to respond to the dynamic change of feed seawater temperature, feed seawater concentration, recycle stream mass flow rate, and steam temperature. The dynamic response curves of TBT (top brine temperature), BBT (bottom brine temperature), the temperature of flashing brine at previous stages, and distillate mass flow rate at previous stages were obtained, which specifically reflect the dynamic characteristics of the system. The presented dynamic model and its treatment can provide better analysis for the real-time optimal operation and control of the MSF system to achieve lower operational cost and more stable freshwater quality.

scholarly journals Experimental Verification of Real-Time Flow-Rate Estimations in a Tilting-Ladle-Type Automatic Pouring Machine

2021 ◽  
Vol 11 (15) ◽  
pp. 6701
Author(s):  
Yuta Sueki ◽  
Yoshiyuki Noda
Keyword(s):  
Real Time ◽  
Molten Metal ◽  
Flow Rate ◽  
Kalman Filters ◽  
Estimation Method ◽  
Estimation Methods ◽  
Liquid Volume ◽  
Model Parameters ◽  
Rate Estimation ◽  
Time Flow

This paper discusses a real-time flow-rate estimation method for a tilting-ladle-type automatic pouring machine used in the casting industry. In most pouring machines, molten metal is poured into a mold by tilting the ladle. Precise pouring is required to improve productivity and ensure a safe pouring process. To achieve precise pouring, it is important to control the flow rate of the liquid outflow from the ladle. However, due to the high temperature of molten metal, directly measuring the flow rate to devise flow-rate feedback control is difficult. To solve this problem, specific flow-rate estimation methods have been developed. In the previous study by present authors, a simplified flow-rate estimation method was proposed, in which Kalman filters were decentralized to motor systems and the pouring process for implementing into the industrial controller of an automatic pouring machine used a complicatedly shaped ladle. The effectiveness of this flow rate estimation was verified in the experiment with the ideal condition. In the present study, the appropriateness of the real-time flow-rate estimation by decentralization of Kalman filters is verified by comparing it with two other types of existing real-time flow-rate estimations, i.e., time derivatives of the weight of the outflow liquid measured by the load cell and the liquid volume in the ladle measured by a visible camera. We especially confirmed the estimation errors of the candidate real-time flow-rate estimations in the experiments with the uncertainty of the model parameters. These flow-rate estimation methods were applied to a laboratory-type automatic pouring machine to verify their performance.

Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Yanfang Wang ◽  
Saeed Salehi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Field Data ◽  
Ann Model ◽  
Time Data ◽  
Neural Network Approach ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Artificial Neural Network Ann ◽  
Pump Pressure

Real-time drilling optimization improves drilling performance by providing early warnings in operation Mud hydraulics is a key aspect of drilling that can be optimized by access to real-time data. Different from the investigated references, reliable prediction of pump pressure provides an early warning of circulation problems, washout, lost circulation, underground blowout, and kicks. This will help the driller to make necessary corrections to mitigate potential problems. In this study, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab. Following the determination of the optimum model, the sensitivity analysis of input parameters on the created model was investigated by using forward regression method. Next, the remaining data from the selected well samples was applied for simulation to verify the quality of the developed model. The novelty is this paper is validation of computer models with actual field data collected from an operator in LA. The simulation result was promising as compared with collected field data. This model can accurately predict pump pressure versus depth in analogous formations. The result of this work shows the potential of the approach developed in this work based on NN models for predicting real-time drilling hydraulics.

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

Use Of Fiber Materials to Mitigate Lost Circulation During Cementing Operations in Turkmenistan

2021 ◽  
Author(s):  
Amanmammet Bugrayev ◽  
Svetlana Nafikova ◽  
Salim Taoutaou ◽  
Guvanch Gurbanov ◽  
Maksatmyrat Hanov ◽  
...  
Keyword(s):  
Volume Fraction ◽  
Control Treatment ◽  
Cement Slurry ◽  
Lost Circulation ◽  
Innovative Materials ◽  
Primary Cementing ◽  
Fit For Purpose ◽  
Fiber Materials ◽  
Fracture Gradient ◽  
Application Requirements

Abstract Lost circulation in depleted sands during a primary cementing job is a serious problem in Turkmenistan. The uncertainty in formation pressure across these sands increases the risk of losses during drilling and cementing, which results in remedial operations and nonproductive time. The need to find a fit-for-purpose lost circulation solution becomes even more critical in an environment with narrow pore pressure-to-fracture gradient, where each cement job with losses compromises the downhole well integrity. An engineered lost circulation solution using innovative materials in the cement slurry was carefully assessed and qualified in the laboratory for each case to optimize the formulation. The lost circulation control treatment combines specialized engineered fibers with sized bridging materials to increase the effectiveness of treatment, formulated and added to the cement slurries based on the slurry solids volume fraction (SVF). Cement slurries with low SVF were treated with higher concentrations of the product and slurries with high SVF used lower concentrations. More than 50 jobs were performed with cement slurries designed at various densities and SVF up to 58% and using this advanced lost circulation material (LCM) to mitigate losses during cementing. Field experience showed positive results, where the differential pressure up to 2,800 psi was expected during cementing operation. A local database, generated based on the design and development work performed, enabled improved decision-making for selection and LCM application requirements for subsequent jobs and development of a lost circulation strategy. The mitigation plan was put in place against losses in critical sections and depleted sand formations in Turkmenistan. It assisted in meeting the cement coverage requirements on numerous occasions, improving overall the integrity of the wells and thus, was considered to be a success. This paper provides insight of this advanced LCM, its application in cement slurries, the logic behind the developed loss circulation strategy, and the high success rate of its implementation. Three case histories are presented to demonstrate the strategy and results.

Sign in / Sign up

Export Citation Format

Share Document