Description of a High-Profit Combination of Low-Cost Real-Time Survey Management Practices Used to Optimize Reservoir Landing in Unfamiliar Deep Offshore Geological Environment

2021 ◽  
Author(s):  
Fabien Momot ◽  
Marie-Jocelyn Comte ◽  
Chloé Lacaze ◽  
Anas Sikal ◽  
Efficience Balou ◽  
...  

Abstract After a first part of the drilling campaign, including about 10 wells and branches achieved within two years, the operator started questioning the geological reservoir model and reserves implications for the field Offshore Congo. Considering the potential economic impact of this development, the decision was made to reduce wellbore positioning uncertainty relying on optimization and survey QAQC processes that could be applied without adding cost of extra equipment, operational time or personnel. With more than 10 wells drilled using recent while drilling measurement and directional tools in the same environment, a wide range of wellbore positioning information was available for analysis, post-correction, and geological/reservoir model deeper understanding. Also, investigation was done to recover existing geomagnetic data acquired during the geophysical campaign. Thanks to this extensive data set, enhanced wellbores positioning was implemented using meticulous combination of processes. The "process" overall impact is often underestimated while most of the data is already available. For lateral positioning correction, it included the processing of geomagnetic IFR data over the Moho field associated to Multi Station Correction. For vertical repositioning, BHA sag correction was applied with scrutinous assessment of residual sag uncertainty and detailed analysis of continuous survey data. This robust, cost-effective, and valuable solution was chosen to be applied by the operator in the Moho field. The process was first applied post-drilling to evaluate the level of improvement that could be brought to another well also exposed to challenging trajectory context (ERD 2 with reduced target 25 × 50 m at almost 8000m MD/RT). It confirmed that the achievable uncertainty reduction would meet well objectives without adding any risk or operational time nor jeopardizing wellbore positioning and collision avoidance. Thus, it brought up to 50 to 60% of uncertainty reduction and about 30m lateral and 3m vertical displacement. The reduction of the uncertainty and trajectory adjustment allowed to enhance geologic context understanding. The vertical position of the well was offset following this revision. This had a 5% consequence in term of oil layer thickness for this well. Then, the team designed and rolled out to the operator and contractors an execution strategy and operational workflow including remote monitoring with near real-time survey QAQC that would ensure the best correction process customized for the specific drilling challenges. This monitoring enabled reducing the ellipsoid to ~20 by 50m radius at TD = 7618m. This allowed entering in the reservoir at the exact top of the structure, behind the fault that was the optimum in term of reserves and secured 90% of potential reserves of this well. The operator's choice of valuing the available information to enhance their asset is a very interesting way to optimize the past efforts put in wellbore positioning to face the current economically constrained environment.

2017 ◽  
Vol 17 (4) ◽  
pp. 850-868 ◽  
Author(s):  
William Soo Lon Wah ◽  
Yung-Tsang Chen ◽  
Gethin Wyn Roberts ◽  
Ahmed Elamin

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.


2018 ◽  
Vol 9 (1) ◽  
pp. 6-18 ◽  
Author(s):  
Dario Cazzato ◽  
Fabio Dominio ◽  
Roberto Manduchi ◽  
Silvia M. Castro

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.


2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ali Hmidet ◽  
Olfa Boubaker

In this paper, a new design of a real-time low-cost speed monitoring and closed-loop control of the three-phase induction motor (IM) is proposed. The proposed solution is based on a voltage/frequency (V/F) control approach and a PI antiwindup regulator. It uses the Waijung Blockset which considerably alleviates the heaviness and the difficulty of the microcontroller’s programming task incessantly crucial for the implementation and the management of such complex applications. Indeed, it automatically generates C codes for many types of microcontrollers like the STM32F4 family, also used in this application. Furthermore, it offers a cost-effective design reducing the system components and increasing its efficiency. To prove the efficiency of the suggested design, not only simulation results are carried out for a wide range of variations in load and reference speed but also experimental assessment. The real-time closed-loop control performances are proved using the aMG SQLite Data Server via the UART port board, whereas Waijung WebPage Designer (W2D) is used for the web monitoring task. Experimental results prove the accuracy and robustness of the proposed solution.


2020 ◽  
Author(s):  
Lavinia Tunini ◽  
David Zuliani ◽  
Paolo Fabris ◽  
Marco Severin

<p>The Global Navigation Satellite Systems (GNSS) provide a globally extended dataset of primordial importance for a wide range of applications, such as crustal deformation, topographic measurements, or near surface processes studies. However, the high costs of GNSS receivers and the supporting software can represent a strong limitation for the applicability to landslide monitoring. Low-cost tools and techniques are strongly required to face the plausible risk of losing the equipment during a landslide event.</p><p>Centro di Ricerche Sismologiche (CRS) of Istituto Nazionale di Oceanografia e di Geofisica Sperimentale OGS in collaboration with SoluTOP, in the last years, has developed a cost-effective GNSS device, called LZER0, both for post-processing and real-time applications. The aim is to satisfy the needs of both scientific and professional communities which require low-cost equipment to increase and improve the measurements on structures at risk, such as landslides or buildings, without losing precision.</p><p>The landslide monitoring system implements single-frequency GNSS devices and open source software packages for GNSS positioning, dialoguing through Linux shell scripts. Furthermore a front-end web page has been developed to show real-time tracks. The system allows measuring real-time surface displacements with a centimetre precision and with a cost ten times minor than a standard RTK GPS operational system.</p><p>This monitoring system has been tested and now applied to two landslides in NE- Italy: one near Tolmezzo municipality and one near Brugnera village. Part of the device development has been included inside the project CLARA 'CLoud plAtform and smart underground imaging for natural Risk Assessment' funded by the Italian Ministry of Education, University and Research (MIUR).</p>


2020 ◽  
Vol 1 (1) ◽  
pp. 35-42
Author(s):  
Péter Ekler ◽  
Dániel Pásztor

Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni. A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási területet. Summary. In the past years artificial intelligence has seen several improvements, which drove its usage to grow in various different areas and became the focus of many researches. This can be attributed to improvements made in the learning algorithms and Big Data techniques, which can provide tremendous amount of training. The goal of this paper is to summarize the current state of artificial intelligence. We present its history, introduce the terminology used, and show technological areas using artificial intelligence as a core part of their applications. The paper also introduces the security concerns related to artificial intelligence solutions but also highlights how the technology can be used to enhance security in different applications. Finally, we present future opportunities and possible improvements. The paper shows some general artificial intelligence applications that demonstrate the wide range usage of the technology. Many applications are built around artificial intelligence technologies and there are many services that a developer can use to achieve intelligent behavior. The foundation of different approaches is a well-designed learning algorithm, while the key to every learning algorithm is the quality of the data set that is used during the learning phase. There are applications that focus on image processing like face detection or other gesture detection to identify a person. Other solutions compare signatures while others are for object or plate number detection (for example the automatic parking system of an office building). Artificial intelligence and accurate data handling can be also used for anomaly detection in a real time system. For example, there are ongoing researches for anomaly detection at the ZalaZone autonomous car test field based on the collected sensor data. There are also more general applications like user profiling and automatic content recommendation by using behavior analysis techniques. However, the artificial intelligence technology also has security risks needed to be eliminated before applying an application publicly. One concern is the generation of fake contents. These must be detected with other algorithms that focus on small but noticeable differences. It is also essential to protect the data which is used by the learning algorithm and protect the logic flow of the solution. Network security can help to protect these applications. Artificial intelligence can also help strengthen the security of a solution as it is able to detect network anomalies and signs of a security issue. Therefore, the technology is widely used in IT security to prevent different type of attacks. As different BigData technologies, computational power, and storage capacity increase over time, there is space for improved artificial intelligence solution that can learn from large and real time data sets. The advancements in sensors can also help to give more precise data for different solutions. Finally, advanced natural language processing can help with communication between humans and computer based solutions.


Biosensors ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 232
Author(s):  
Magdalena R. Raykova ◽  
Damion K. Corrigan ◽  
Morag Holdsworth ◽  
Fiona L. Henriquez ◽  
Andrew C. Ward

Antimicrobial drug residues in food are strictly controlled and monitored by national laws in most territories. Tetracyclines are a major broad-spectrum antibiotic class, active against a wide range of Gram-positive and Gram-negative bacteria, and they are the leading choice for the treatment of many conditions in veterinary medicine in recent years. In dairy farms, milk from cows being treated with antibiotic drugs, such as tetracyclines, is considered unfit for human consumption. Contamination of the farm bulk tank with milk containing these residues presents a threat to confidence of supply and results in financial losses to farmers and dairy. Real-time monitoring of milk production for antimicrobial residues could reduce this risk and help to minimise the release of residues into the environment where they can cause reservoirs of antimicrobial resistance. In this article, we review the existing literature for the detection of tetracyclines in cow’s milk. Firstly, the complex nature of the milk matrix is described, and the test strategies in commercial use are outlined. Following this, emerging biosensors in the low-cost biosensors field are contrasted against each other, focusing upon electrochemical biosensors. Existing commercial tests that identify antimicrobial residues within milk are largely limited to beta-lactam detection, or non-specific detection of microbial inhibition, with tests specific to tetracycline residues less prevalent. Herein, we review a number of emerging electrochemical biosensor detection strategies for tetracyclines, which have the potential to close this gap and address the industry challenges associated with existing tests.


Plant Disease ◽  
2016 ◽  
Vol 100 (3) ◽  
pp. 640-644
Author(s):  
Patricia A. Okubara ◽  
Natalie Leston ◽  
Ute Micknass ◽  
Karl-Heinz Kogel ◽  
Jafargholi Imani

Rhizoctonia solani AG8, causal agent of Rhizoctonia root rot and bare patch in dryland cereal production systems of the Pacific Northwest United States and Australia, reduces yields in a wide range of crops. Disease is not consistently controlled by available management practices, so genetic resistance would be a desirable resource for growers. In this report, we describe three rapid and low-cost assays for R. solani AG8 resistance in wheat and barley, with the view of facilitating screens for genetic resistance in these hosts. The first assay uses 50-ml conical centrifuge tubes containing soil infested with R. solani AG8 on a substrate of ground oats. The second assay uses roots of 3-day-old seedlings directly coated with infested ground oats, followed by incubation in plastic dishes. The third assay, suitable for barley, uses whole infested oat kernels in 50-ml tubes. Symptoms are quantified on the bases of root fresh weight and total root length at 7 and 3 days for the tube and coating assays, respectively. Each of the assays show the same disease differential between susceptible and partially resistant wheat genotypes. The assays can be conducted in the laboratory, growth chamber, or greenhouse.


Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7033
Author(s):  
Zhitian Li ◽  
Wuhao Yang ◽  
Xingyin Xiong ◽  
Zheng Wang ◽  
Xudong Zou

Non-contact and non-destructive acceleration measurement is receiving considerable attention due to their low cost, flexibility, and simplicity of implementation, as well as their excellent performance in some emerging applications such as medical electronics applications, vibration monitoring, and some other special scenarios. In this paper, a visual accelerometer system based on laser speckle optical flow detection named Viaxl is proposed. Compared with the conventional non-contact acceleration measurement method based on a laser system, Viaxl has moderate and stable performance with the advantages of low cost and simplicity of implementation. Experiment results demonstrate that Viaxl, which consists of a commercial camera and a low-cost laser pointer, can achieve real-time, non-contact acceleration measurement, and confirm the basic system performance of Viaxl: a measurement nonlinearity better than 1.3%, up to 31 dB signal-to-noise ratio, and 1150 Hz theoretic bandwidth; this demonstrates the huge potential of Viaxl in a wide range of applications, and provides a new possible technical method for non-contact acceleration detection.


Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3293
Author(s):  
Gustavo Costa Gomes de Melo ◽  
Igor Cavalcante Torres ◽  
Ícaro Bezzera Queiroz de Araújo ◽  
Davi Bibiano Brito ◽  
Erick de Andrade Barboza

Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices’ clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems.


Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Nicola M. Capstaff ◽  
Claire Domoney ◽  
Anthony J. Miller

Abstract Background Management regime can hugely influence the efficiency of crop production but measuring real-time below-ground responses is difficult. The combination of fertiliser application and mowing or grazing may have a major impact on roots and on the soil nutrient profile and leaching. Results A novel approach was developed using low-cost ion-selective sensors to track nitrate (NO3−) movement through soil column profiles sown with the forage crops, Lolium perenne and Medicago sativa. Applications of fertiliser, defoliation of crops and intercropping of the grass and the legume were tested. Sensor measurements were compared with conventional testing of lysimeter and leachate samples. There was little leaching of NO3− through soil profiles with current management practices, as monitored by both methods. After defoliation, the measurements detected a striking increase in soil NO3− in the middle of the column where the greatest density of roots was found. This phenomenon was not detected when no NO3− was applied, and when there was no defoliation, or during intercropping with Medicago. Conclusion Mowing or grazing may increase rhizodeposition of carbon that stimulates soil mineralization to release NO3− that is acquired by roots without leaching from the profile. The soil columns and sensors provided a dynamic insight into rhizosphere responses to changes in above-ground management practices.


Sign in / Sign up

Export Citation Format

Share Document