Well Logging is a technique to collect data from the borehole to determine subsurface formations' physical properties. Also known as wireline logging, it is an indirect method of evaluation, as there is no direct contact with the formations. The technique allows us to determine the reservoir properties of rocks present beneath the surface of the Earth. Well logging is a key technique used in the exploration of minerals and O&G.
A well log is typically a record of the formations or events that are encountered during drilling. Sometimes it is also referred to as borehole logging.
Well logs are recognised as legal papers, which captures the activities - a well from the drilling stages to its completion.
A well log can be prepared by two different processes, i.e. by visual inspection of drill cuttings or specially-designed logging equipment, which can be lowered into the borehole to record various events.
The well log, which is prepared by the visual inspection of rock chip samples, is known as Geological Log, whereas the log prepared by using logging units is known as geophysical well logs.
The technique is primarily used for the evaluation of geological formation. The evaluation has wide applications in groundwater, hydrocarbon, and mineral exploration.
During the geophysical well logging, the logging tool is inserted into the borehole with the help of a wireline. A typical logging tool consists of a transmitter and a receiver, which is used to transmit and receive the signals.
Depending on the type of logging tool, relevant energy is transmitted to the formation by the transmitter. Upon reflection from the formation, the energy is recorded back at the receiver of the tool. The reflected energy carries useful information about the formation with them. It must be noted that this is an indirect technique of formation evaluation, which means the technique doesn't record any physical property of the formation on a direct measure basis.
The information collected at the receiver is then transmitted to the surface with cables, which can be decoded in software for further interpretation by geologists and petrophysicists.
The logging tools are designed to bring the various type of information required for formation evaluation by geoscientists. This may range from physical to chemical and structural information about the subsurface formations. Some of the major information that can be revealed from the well logging techniques are:
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Characteristics of Rock: It means one can determine the porosity, permeability, and saturation of rocks, to evaluate the volumetric space, where the hydrocarbons or minerals are accumulated.
Composition of Rocks: It means geologists can determine the type of rock present at a specific depth. Either, it's shale, sandstone or limestone.
Saturation: The petrophysicists can find the amount of saturation, which means the void space volume that is accommodated by liquids.
The trajectory of the Borehole: The technique can also tell us about the size, shape and trajectory of the borehole.
The integrity of Rocks: In addition to all the above advantages, the technique is well versed to identify cavings and other structural weaknesses along the wall of the borehole, which may lead to various potential borehole problems.
Conrad Schlumberger first started the science of well logging in 1927 as part of his work related to the Earth's resistivity measurements for surface exploration. Later on, the advancement in technology and more precise results made the technology more popular. The demand rose rapidly internationally.
Various types of well logs can be recorded with the help of logging tools, depending on the scope of requirement. There is a huge list of types of well logs that are used in the industry today. We will briefly discuss some most prominent logs, which are frequently used in the industry by experts.
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Well Logging has a wide range of applications from mining to oil and gas exploration. The technique is also used for groundwater exploration. The method has majorly following applications:
What is Data Analytics? Data Analytics involves a set of quantitative and qualitative approaches and processes that can be used to determine useful information for business decision-making. The process involves various patterns and techniques, including: extracting a raw database, and categorising it to identify and analyse the behaviour, relation and connection of the results. The ultimate goal is to acquire valuable information in order to make decisions for businesses’ benefit and productivity. In today's competitive times, most companies chalk out their business plan with the help of data analytics. With organisations becoming customer-service oriented, data analytics has become a critical tool to reach the target audience in an effective manner while understanding their requirements. Once data is collected, it is analysed and stored according to organisations’ requirements. The data analysis process has multiple layers involved, and its diverse modules are not just used in businesses but also in science and social science fields. Rather than making decisions based on just available information, one can utilise data analytics in examining the data in standard ways and churning out the results from it. It has been observed that companies generally make decisions based on past references and future outcomes. Data analytics appears advantageous in providing useful information towards this end. Why do Businesses Need to Use Data Analytics? Many data analytics’ tools and softwares are readily available these days. These systems use resources, such as machine learning algorithms and automation. Data scientists and analysts are counted amongst the leading career options as well. These professionals use data analytics techniques while researching and presenting useful information for businesses to increase productivity and gain. The process helps companies understand their target audience and determine effective ways to cater to their needs. Data analytics can further be used to design strategies in marketing campaigns and promotions and also evaluate its results. Data analytics is primarily used in business-to-consumer (B2C) processes to boost business performance and improve the bottom line. There are data collection firms which gather consumer information and provide it to the businesses so that the companies can effectively influence the market. The collected data is not only used to understand and impact consumer behaviour but also determine market economics and its practical implementation. The data used in the process can be either be data collected in the past or newly updated data. There are various methods to manage consumer and market information. It may come directly from the customers or potential customers or can be purchased from the data collection vendors. The data primarily includes audience demographics, behavioural patterns and expense threshold. How Can Data Analytics be Effectively Used in Business Processes? Data analytics is an ever-evolving technique. Earlier, the data was collected manually, but with the rise of internet and technology, data is now collected online with the help of search engines and social media platforms. Subsequently, the information is analysed through available software. Here is a list of some key steps businesses can follow to leverage the benefits of data analytics: Set up crucial metrics: This step reduces the guesswork and provide data-based insights to the businesses. Before embarking on the data analytics process, it is vital to determine the goal for your business. Analysing customer data helps in understanding conversion rate, consumer spending ability, demographics etc. The results of the analysis can support the businesses while making decisions in launching an advertising or marketing campaign. Similarly, the unwanted data can be erased from the database so that the brands can focus on their right target audience. The relevant metrics will change the course of the company and push it in the right direction. Moreover, once your key metrics are set, even when the market conditions change in the future, you can adjust the metrics according to the requirement and achieve the results. Set a clear module: It is important to examine the data correctly by avoiding common mistakes. An ambiguous path can produce confusing insights while wasting time and energy of businesses. Therefore, it is recommended to draw a clear goal in order to achieve actionable insights. The data, when collected from different sources, need to be merged accurately in the analytics model. Businesses can modulate their data analytics systems either manually or through automation. There are various data modelling practices available in the market. The best use of these techniques can simplify the process of modelling complex data. Data visualisation: Once the relevant data is collected, and the modules are set to analysis, visualisation of that data will assist in understanding the information correctly. When the businesses have an acute knowledge of what their target audience wants, they can then focus on strategising advertisement and content, which matches the consumers' interest. It is the critical step in the data analytics process to distinguish insights from information. Not everyone is comfortable dealing with numbers. Hence, ensuring that key stakeholders understand essential points and information can be displayed in a visually appealing format seem crucial to capitalise on data effectively. Right tools to implement insights:Having access to data and insights can get overwhelming. However, the information is worthless if the businesses are unable to implement it successfully. While it is important to collect the data and set critical metrics and modules to analyse it, it is also imperative to translate the data into practical actions. The eventual goal is to improve sales or grow profits. It is ultimately in the marketers' hands to transform the gained insights into a successful implementation. The consumers' insights should be incorporated while establishing a marketing plan and at all decision-making steps.
What is data warehousing? Data warehousing is defined as the method of gathering & handling data from different sources to get meaningful output and insights. Data warehousing is central to the BI system and is built for data analysis and reporting. Source: © nfo40555 | Megapixl.com In simple terms, a data warehouse is a large collection of data utilized by businesses to make investment decisions. What are the characteristics of data warehousing? Data warehouse has supported businesses in making informed decisions efficiently. Some of its key features are highlighted below: The data in a data warehouse is structured for easy access, and there is high-speed query performance. The end users generally look for high speed and faster response time – two features present in data warehousing. Large amount of historical data is used. Data warehouse provides a large amount of data for a particular query. The data load comprises various sources & transformations. What are the benefits of data warehousing? The Companies which used data warehousing for analytics and business intelligence found several advantages. Below are some of them: Better Data: When data sources are linked to a data warehouse, the Company can collect consistent and relevant data from the source. Also, the user would not have to worry about the consistency and accessibility of the data. Thus, it ensures data quality and integrity for sound decision making. Faster decisions: Through data warehousing, it is possible to make quicker decisions as the data available is in a consistent format. It offers analytical power and a comprehensive dataset to base decisions on tough truths. Thus, the people involved in decision making do not have to rely on hunches, incomplete data, and poor quality data. It also reduces the risk of delivering slow and inaccurate data. How does a data warehouse work? A data warehouse is like a central repository where the data comes from various sources. The data streams into the data warehouse from the transactional system and other relational databases. These data could either be structured, semi-structured or unstructured. These data get processed, altered, and consumed in a way that the end-user can gain access to the processed data in the data warehouse via business intelligence (BI) devices, SQL clients and spreadsheets. A data warehouse merges the data that comes from various sources into a complete database. The biggest advantage of this merged data is that the Company can analyze the data more holistically. It also makes the process of data mining smooth. Copyright © 2021 Kalkine Media Pty Ltd. Component of a data warehouse A data warehouse can be divided into four components. These are: Load Manager Load Manager, also known as the front component, does operations related to the mining and loading the data into a data warehouse. Load manager transforms the data for entering into Data warehouse. Warehouse Manager The warehouse manager manages the data within the data warehouse. It analyses data to confirm that the data in the data warehouse is steady. It also conducts operations such as the creation of indexes and views, generation of denormalization and aggregations, modifying and integrating the source data. Query Manager Query Manager is a backend component that does operations concerning the supervision of user queries. End-User access tools End-User access tools comprise data reporting, query tools, application development tools, EIS tools, data mining tools, and OLAP tools. Roles of Data Warehouse Tools and Utilities The tools and utilities in a data warehouse are used for: Data extraction: The data extraction process involves gathering data from heterogeneous sources. Data cleaning: Data cleaning consists of searching for any error in the data. Data transformation: Data transformation process involves changing the data into a data warehouse setup. Data loading: This process involves data sorting, recapping, consolidating, verifying integrity. Refreshing: This process requires revising data sources to the warehouse. Application of data warehouse Data warehouse plays a considerable role across multiple sectors. Some of the sectors it caters to are highlighted below. Aviation sector In the aviation sector, a data warehouse’s role can be seen in crew assignment, route profitability analysis, any promotional activity. Banking Industry In the banking sector, the focus is on risk management, policy reversal, customer data analysis, market trends, government rules and regulations and making financial decision. Through a data warehouse, banks can manage the resources available on the deck effectively. Banks also take the help of a data warehouse to do market research, analyze the products they offer, develop marketing programs. Retail industry Retailers act as an intermediary between the producers and the customers. Hence, these retailers use a data warehouse to maintain the records of both producers as well as the customer to maintain their existence in the market. Data warehouses help track inventory, advertisement promotions, tracking customer buying trends and many more. Healthcare industry In the healthcare industry, a data warehouse is used to predict the outcome of any test and taking relevant action accordingly. Data warehouses help them to generate patient treatment report, offer medical services, track the medicine inventory. Many patients visiting hospital have health insurance. Through a data warehouse, hospitals maintain the list of insurance providers. Investment and insurance sector In the insurance and investment sector, the role of data warehouse becomes important in tracking the data pattern, customer trend and market movement. Services sector In the services sector, a data warehouse is used for maintaining financial records, studying the revenue pattern, customer profiling, resource management and human resource management. Telecom The telecom sector uses a data warehouse in the promotion of its offerings, making sales decision, distribution decision, features to include in case they decide to launch a new product based on the customer requirement. Hospitality The hospitality sector involves hotel and restaurant services, car rental services etc. In this sector, the companies use a data warehouse to study the customer feedback on the various services offered and accordingly design and evaluate their advertising and promotion campaigns.
What is Day Trading? Day trading is popular among a section of market participants. It is a type of speculation wherein trades are squared-off before the market close in the same day. An individual or a group is engaged in buying and selling of securities for a short period for profits, the trades could be active for seconds, minutes or hours. One can engage in day trading of many securities in the market. Anyone who has sufficient capital to fund the purchase can engage in day trading. For a class of people, day trading is a full-time job. Day traders are agnostic to the long-term implications of the security and motive is to benefit from the price changes on either side and make profit out of the asset price fluctuations within a day. They bet on price movements of the security and are not averse to take short positions to benefit from the fall in price. Day trading is not only popular among individuals or retail traders but institutional traders as well, therefore the price movements are large sometimes depending on the magnitude of information flow and accessibility. Everyone wants to make money faster, and many are inclined to speculate in markets, but it comes with considerable risk and potential loss of capital. People engaged in day trading also incur losses, and oftentimes outcomes are disheartening. Day trading is a risky activity, similar to sports betting and gambling, and it could become addictive just like gambling and sports betting. Since the motive is to earn profits, the profits realised from day trading also tempt people to continue speculating. People spend considerable time and efforts to make the most out of day trading. They have to continuously absorb and incorporate information flow, which has become increasingly accessible driven by new-age communications systems like Twitter, Facebook, forums etc. But not only information flows have been favourable, day traders are now equipped with best in class infrastructure to execute trades even on compact devices like mobile phones. The accessibility to markets is at a paramount level and gone are days of phone call trading and lack of information flows. What are the essentials for Day Trading? Basic knowledge of markets With lack of basic knowledge of markets, day trading may yield unacceptable outcomes. It becomes imperative for people to know what’s on the stake. Prospective day traders should know about capital markets, and the securities traded in capital markets like bonds, equity and derivatives. Buying shares and expecting a return from the price movements are on the to-do list for many. However, it is important to know about and risks and potential returns from speculating in capital markets. After getting some basic knowledge about markets and securities, aspiring day traders should know how to analyse market prices of securities through fundamental analysis and technical analysis. Although day traders don’t practice fundamental analysis extensively, they spend considerable time to apply technical analysis, to formulate a entry and exit strategy. Device and internet connection Trading is now possible on mobile applications as well as computer applications or websites. An aspiring day trader will likely begin with mobile phone given the accessibility, and laptops/computers are useful as scale grows larger and complex. Internet connection is prerequisite to practising day trading, and it is favourable to have a fast internet connection to avoid glitches and potential problems. These perquisites are now available with large sections of societies. Broker and trading platform A broker will facilitate a market for potential trades. The security brokerage industry has also seen a profound shift as technology has driven cost lower while competition is ramping up across jurisdictions. Large retail brokerages have moved towards zero commission trading in the U.S., and the same is seen being the trend across other geographies as well. The entry of discount and online brokerages has perhaps given wings to the retail market participants as well as the retail market for security brokers. Robinhood has grown immensely popular in the United States, but there are many firms like Robinhood in other jurisdictions. Each country has some firms with business model on same lines as Robinhood. Brokers now offer high-quality mobile applications and web services to clients, and trading security has never been so accessible. They also provide access to the global market along with a range of securities, including commodity derivatives, currency derivatives, CFDs, options, futures, bond futures etc. Real-time market information flow On public sources, market price information is at times not live due technical shortcomings, which will not work appropriately, especially for day traders. Brokers not only provide platform and market but several other services, including margin lending, real-time data, research. Day traders closely track prices of securities and overall information flow to incorporate developments in bidding, and real-time data provides accurate prices throughout market hours. Information flow largely relates to the news around the company, industry or economy. Day traders now have far better sources of information than the conventional sources, and sometimes these sources could be exclusive to a group. What are the risks of day trading? Most of the aspiring day traders end up losing money, given the lack of experience and knowledge. They should rather only bet on capital that they are comfortable to loose, in short, they should avoid risk of ruin. Day trading is sort of pure-play speculation and application of knowledge, information flow, laced with good trading system is paramount. The only concern of day traders is movement in price, which contradicts from investments. Day traders try to time and ride the momentum in the price and exit the trade before momentum turns otherwise, which can happen frequently. It consumes considerable time and induces stress on the individuals given the nature of security prices, which can move north and south abruptly throughout the day, hours, minutes and seconds. Day traders should have enough capital to trade in cash instead of margin. Day trading on margin or borrowed money is extremely risky and has the potential to make a person insolvent, especially in cases of extreme risk-taking. The leverage associated with borrowed money magnifies profits as well as losses. Aspiring day traders should equip themselves with adequate knowledge, competency and sound risk management process. Although fast money is dear to most, it is better to know what is at stake before jumping into markets with excitement.
Earmarking is a term used in the banking industry to refer to funds, which has been set aside to pay for some specific projects. The term is usually associated with transactions as well as earmarked transactions, which could be defined as the business transaction, made to claim any pre-allocated fund to meet the expenditure.