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Click here to view this data in an advanced geospatial platform that is instantly available free of charge to guest users of geoLOGIC’s gDC Cloud ➡️: https://lnkd.in/gziD5qzz. To gain unlimited access to view wells coming off confidential status, register for a free account: https://lnkd.in/gRU8As8i 💡 Over 1,750 wells are coming off confidential status in the remainder of 2024. This information is watched very closely by industry, as technical data (such as producing hours, LAS logs, tests, cores, completion details, drilling tour reports) when released provide crucial insight into operators’ performance including an assessment on how successful they have been in turning undeveloped resources into reserves or expanding existing development areas. 🔳 The 1,754 wells that come off confidential status in 2024 Q2 to Q4 target numerous formations, with the Montney having the highest number of wells coming off confidentiality (443), followed by the Clearwater (355). The Sparky, Viking and Duvernay formations round out the top five, each with just under 100 wells on confidential status. A recent uptick in activity for Mannville, Ellerslie and Charlie Lake put them on the list as well. 🔳 The top operators on the confidential well list (by well count for the Q2 to Q4 period) are the Clearwater operators, as exploration and development continues. In the Clearwater, Rubellite Energy tops the list with 179 wells, followed by Spur Petroleum with 92 and Tamarack Valley Energy with 48 wells. Generally, in North America, regulators make well data available, including producing hours, tests, completions, formations, logs, etc., after the period of confidentiality defined in the licensing agreement. The longest lateral length was drilled by Archer Exploration Corp. at 100/01-17-071-08W6 in the Elmworth area and targeted the Charlie Lake formation. This Charlie Lake well was spud on Jan. 3, 2024, and drilled to a total measured depth of 8,262 metres with the lateral being 5,892.9 metres. The well was put on production on Feb. 18, 2024, and has produced 53,641 boe to-date (~78% oil). Data is functionally released from confidential status weekly in Alberta and B.C. and on the day confidential status ends in Saskatchewan. If you have questions about how you can use gDC Cloud to enable your OFS workflows, email us at 📧 Sales@geologic.com or call us at 📞 1 855-870-1700. #cloud #clouddata #oilandgas #energy #analysis #insights #oilgas #wells #confidentialwell #Montney #Clearwater

Click here to view this data in an advanced geospatial platform that is instantly available free of charge to guest users of geoLOGIC’s gDC Cloud ➡️: https://lnkd.in/gnZsZgNr. To gain unlimited access and time to view this insight and previous insights at your leisure, register for a free account here 💻: https://lnkd.in/gRU8As8i Veren Inc. (formerly Crescent Point) announced on May 10 that it has drilled Canada’s longest onshore well, reported DOB Energy. The Crescent Point 100/07-16-065-23W5 Duvernay well was spud on Jan. 23, 2024, and it was drilled to a total measured depth (MD) of 9,017 metres, with a lateral length of 5,432 metres. The Duvernay play dominates the top 50 longest wells drilled (MD) list with 44 of the top 50 longest wells drilled in the Kaybob/Fox Creek area. With a typical depth of 3,200 m (about two miles) to reach the top of the Duvernay, the vertical section accounts for a significant portion of these long drills. The top 50 longest wells drilled by operator well count include: ·        Chevron Canada Ltd. (13) ·        Crescent Point Energy Corp. (12) ·        Kiwetinohk Energy Corp. (10) ·        Paramount Resources Ltd. (4) ·        Archer Exploration Corp. (3) ·        Whitecap Resources Inc. (3) ·        Canadian Natural Resources Limited (2) ·        PetroChina Canada Ltd. (2) ·        NuVista Energy Ltd. (1) The target formations of the top 50 longest wells drilled include the Duvernay (44), Charlie Lake (3), and Montney (3). The longest lateral length was drilled by Archer Exploration Corp. at 100/01-17-071-08W6 in the Elmworth area and targeted the Charlie Lake formation. This Charlie Lake well was spud on Jan. 3, 2024, and drilled to a total measured depth of 8,262 metres with the lateral being 5,892.9 metres. The well was put on production on Feb. 18, 2024, and has produced 53,641 boe to-date (~78% oil). If you have questions about how you can use gDC Cloud to enable your OFS workflows, drop us a line at 📧  Sales@geologic.com or call us at 📞 1 855-870-1700. #cloud #clouddata #oilandgas #energy #analysis #insights #oilgas #wells #WCSB #drilling

🔹 𝐇𝐢𝐝𝐝𝐞𝐧 𝐓𝐫𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐁𝐞𝐧𝐞𝐚𝐭𝐡 𝐨𝐮𝐫 𝐟𝐞𝐞𝐭 𝐰𝐢𝐭𝐡 𝐒𝐞𝐢𝐬𝐦𝐢𝐜 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐭𝐢𝐨𝐧! 🌏🔍 ✔️ Seismic interpretation is a critical technique in the oil and gas industry, unlocking the secrets of the subsurface. Here’s a quick overview of its significance: 🔸 𝙒𝙝𝙖𝙩 𝙞𝙨 𝙎𝙚𝙞𝙨𝙢𝙞𝙘 𝙄𝙣𝙩𝙚𝙧𝙥𝙧𝙚𝙩𝙖𝙩𝙞𝙤𝙣? » It's the process of analyzing seismic data to create a detailed image of the subsurface. Geophysicists and geologists use this information to identify potential hydrocarbon reservoirs, faults, and other geological structures. 🔸 𝙒𝙝𝙮 𝙞𝙨 𝙞𝙩 𝙄𝙢𝙥𝙤𝙧𝙩𝙖𝙣𝙩? 1-Resource Identification: Helps in locating oil and gas reserves. 2-Risk Reduction: Minimizes drilling risks by providing a clearer understanding of the subsurface. 3-Efficient Exploration: Guides exploration efforts, saving time and resources. 🔸 𝙆𝙚𝙮 𝙎𝙩𝙚𝙥𝙨 𝙞𝙣 𝙎𝙚𝙞𝙨𝙢𝙞𝙘 𝙄𝙣𝙩𝙚𝙧𝙥𝙧𝙚𝙩𝙖𝙩𝙞𝙤𝙣: 📌Data Acquisition: Collecting seismic data using various methods. 📌Data Processing: Enhancing the quality of the seismic data. 📌Seismic Mapping: Creating maps and models of the subsurface. 📌Analysis & Interpretation: Understanding the geological features and potential reservoirs. 🔸 𝙏𝙤𝙤𝙡𝙨 & 𝙏𝙚𝙘𝙝𝙣𝙞𝙦𝙪𝙚𝙨: • Seismic Attributes: Enhance the visibility of geological features. • 3D Visualization: Provides a comprehensive view of the subsurface. • Geophysical Software: Programs like Petrel, Kingdom, and GeoFrame facilitate the interpretation process. 🔸 𝙁𝙪𝙩𝙪𝙧𝙚 𝙤𝙛 𝙎𝙚𝙞𝙨𝙢𝙞𝙘 𝙄𝙣𝙩𝙚𝙧𝙥𝙧𝙚𝙩𝙖𝙩𝙞𝙤𝙣: » The integration of AI and machine learning is revolutionizing seismic interpretation, making it faster and more accurate. The future holds immense potential for more precise and efficient resource discovery. #Seismic_Interpretation #Oil_and_Gas #Geophysics #Exploration #Geology #Energy_Industry #AI_in_Geophysics #Resource_Discovery

With the course Tesseral Ai is sharing online, we plan to go into deep water seismic. Having worked for a successful offshore company and been involved with offshore seismic acquisition want to include this in the course Tesseral Ai is offering free. It is happening as more oil and gas companies are planning to drill offshore because of the lower carbon, decrease in the BEP in offshore wells to around $30 per barrel which makes it competitive with shale plays due to the use of FPSO’s or tying back to nearby wells, and shale plays decline rate is high compared to offshore wells that produce for decades. The use of subsea tiebacks to nearby existing production facilities has allowed producers to reduce both project costs and start-up times. This has been done by leveraging existing infrastructure to reduce the time for a well to come onstream. The time to bring a deepwater well onstream has been reduced to 3 years compared to BP Thunderhorse taking 9 years to come onstream. Subsea processing technologies has enabled long-distance tieback opportunities that can exceed more than 33 km for remote and marginal fields. This is covered in the attached write up. Tying back to existing infrastructure in the oilsands has also lowered its BEP from $78 per barrel to $45 per barrel. Oilsands decline rates are less than shale plays as well which makes investment into oilsands more attractive. As the TMX comes online oilsands companies have increased the number of people working the oilsands and the new phases of oil sands being developed are smaller, incremental brownfield expansions, rather than large-scale greenfield projects. They are also tying these developments into existing central processing facilities (CPFs), rather than building new CPFs. Operating costs have also fallen because of increased reliability, with less downtime and increased throughput Production in the oilsands are expected to peak with all the new pipeline capacity compared to 5 years ago. This addition pipeline capacity is coming from the Minnesota Line 3 pipeline expansion increasing its capacity from 380,000 BOPD to 760,000 BOPD in 2021 and the TMX pipeline expansion increasing capacity from approximately 300,000 BOPD to 890,000. With the increase in pipeline capacity we will see WCS differential decrease and oilsands companies will increase production to meet the demand to keep the pipelines full. Part of the increase in demand for WCS is due to sanctions on Iran and Venezuela and oil supply from Latin countries will decrease for several reasons. Oil exports from Mexico are expected to decrease by 70% between 2021 and 2023 due to political reasons. In Colombia, the lack of major conventional oil and natural gas discoveries over the last decade has left the country with meager proven reserves of 1.8 billion barrels, which is sufficient to only support oil production for another six years.

ParaView vs. geological modeling software ParaView is exceptional for data visualization and analysis. But it lacks some specialized geological modeling capabilities. ParaView is focused on rendering and visualizing 3D datasets. It's not design for creating geological models from scratch. It doesn’t offer advanced stratigraphic modeling, well log correlation, or structural modeling. These are essential for building realistic subsurface models. It doesn’t provide built-in support for certain geological data formats or workflows. Reservoir simulation modeling, for example, is part of Petrel or GoCAD. Petrel is often used in the oil and gas industry. These modules may help predict fluid flow and reservoir performance. ParaView doesn’t offer the same level of workflow integration and project management.  Yet, there is something unique it offers. By stacking a sequence of filters, its user creates a pipeline. After that raw data files can be changed for new ones. This allows creating own workflows which are flexible and can be adjusted when needed. For more visit Open Geomodeling YouTube channel: https://lnkd.in/d5A4W8KX #geology #geotechnical

𝗖𝗼𝗺𝗺𝗼𝗻 𝗠𝗶𝗱𝗽𝗼𝗶𝗻𝘁 (𝗖𝗠𝗣) 𝗠𝗲𝘁𝗵𝗼𝗱 The CMP method involves multiple seismic sources and receivers arranged in such a way that multiple seismic traces can be gathered for a common midpoint on the subsurface. This common midpoint is the point on the subsurface directly beneath the midpoint between the source and receiver on the surface. By collecting data from multiple source-receiver pairs that share this common midpoint, geophysicists can stack these traces to enhance the signal-to-noise ratio and produce a clearer subsurface image. Steps in the CMP Method 1. Data Acquisition: Seismic waves are generated using a source, such as a dynamite explosion or a seismic vibrator. These waves travel through the subsurface and are reflected back to the surface where they are recorded by geophones. This process is repeated multiple times with varying positions of the source and receiver, but always ensuring that the midpoint remains the same. 2. Sorting: The recorded seismic data are sorted into CMP gathers. Each gather contains traces that share the same midpoint but have different source-receiver offsets. This sorting is crucial for the subsequent processing steps. 3. NMO Correction: Normal Moveout (NMO) correction is applied to the CMP gathers. Due to the varying offsets, seismic waves reflected from the same subsurface point will have different travel times. NMO correction compensates for these differences by adjusting the travel times so that reflections from the same subsurface point align correctly. 4. Stacking: After NMO correction, the traces within each CMP gather are stacked, or summed, together. This stacking process enhances the coherent signals (reflections) while suppressing random noise, resulting in a clearer and more accurate subsurface image. 5. Migration: The final step is migration, which adjusts the stacked seismic data to their correct spatial positions. This step corrects for any distortions caused by the dipping of geological layers and provides a more accurate representation of the subsurface structures. Advantages of the CMP Method - Enhanced Signal-to-Noise Ratio: By stacking multiple traces, the CMP method significantly improves the signal-to-noise ratio, making it easier to identify and interpret subsurface reflections. - Increased Resolution: The method allows for a higher resolution of the subsurface image, enabling the detection of smaller geological features that might be missed with single-fold seismic data. - Data Redundancy: The CMP method provides multiple recordings of the same subsurface point, which can be used to cross-check and validate the seismic data, ensuring its reliability and accuracy. Photo refrence, credit : https://lnkd.in/dJSmEmSi

Enhancing well-top prognosis: The Alternative Modeling Technique of Seismic RMS Velocity • In the realm of oil and gas exploration, accurate subsurface prognosis is crucial for identifying potential hydrocarbon reservoirs. Traditionally, the Root Mean Square (RMS) velocity model has been the standard in time-depth conversion. However, recent advancements in seismic velocity modeling offer alternative approaches that can enhance the prognosis for off-depths enhancement, providing more precise and reliable predicted markers. • RMS velocity is an average velocity that is used to convert seismic reflection times to depths. While it is a useful tool in seismic data analysis, it has certain limitations when used for top prognosis and interpretation in geological contexts. Here are some of the main limitations: 1. Velocity Anisotropy: RMS velocity doesn’t account for anisotropy and can lead to significant depth conversion errors due to its homogeneity assumption. 2. Depth Conversion Errors: Since RMS velocity is an average measure, depth conversion using RMS velocity can be prone to errors, especially in regions with significant velocity variations. This can affect the accuracy of the seismic prognosis. 3. Impact of Data Quality: The accuracy of RMS velocity depends on the quality of the seismic data. Poor data quality, noise, and processing artifacts can introduce errors in RMS velocity calculations and subsequent interpretations. • Additionally, RMS velocity oversimplifies complex geological structures. Mitigating these limitations requires using complementary methods like anisotropic based methods, incorporating well-log data, and employing advanced interpolation techniques. Alternative Seismic Velocity Models • To overcome these limitations in our field, where we recorded off depths greater than 100 m, based on RMS velocity. An anisotropic velocity model has been developed. This technique offers distinct advantages for improving the accuracy of drilled tops. A workflow has been built (see below). An acoustic impedance variogram has been used to interpolate an-isotropically the new generated velocity model. The prognosed tops have been improved considerably (less than the seismic resolution of ~ 40 m). Finaly, I'd like to thank our geophysicist advisor for his support: Miguel La Cruz

Seismic data has very important benefits in subsurface modeling. This data helps in accurately mapping geological structures, allowing researchers to identify rock layers, faults, and other geological structures that are not visible from the surface. This is very useful in understanding complex geological structures and determining potential areas for oil and gas or other mineral exploration. Apart from that, the seismic refraction method is often used to determine the lithology or type of rock below the surface, as well as rock layers up to a certain depth. This is very important in determining oil and gas reservoirs and understanding the overall geological conditions. Seismic data also makes it possible to estimate the depth and thickness of rock layers, which is essential in determining drilling locations and developing oil and gas wells. With this information, companies can plan drilling more efficiently and reduce the risk of failure. Furthermore, seismic data is used to create three-dimensional visual models of subsurface structures, providing a clear picture of how underground rock formations are structured and potentially contain hydrocarbons. Through interpretation of seismic data, we can identify structural traps that can harbor hydrocarbon accumulation. Thus, subsurface modeling based on seismic data is very helpful in reducing exploration uncertainty and ensuring that drilling is carried out in the right location, which ultimately increases the efficiency and success of drilling operations and oil and gas production. In ArcGIS Pro we can also model how seismic data works, by processing it using 3D emperical Bayesian kriging and continuing to visualize using a Voxel layer, but it must be noted, when displaying the Voxel layer of seismic data, we need to select the voxel screen type to be continuous, so that we can show how flow of seismic data works. In displaying the seismic data voxel layer, we can also see how it is distributed by creating slices and sections, which we can arrange so that it is easier to see how the seismic data flow works. But in ArcGIS Pro we can only visualize it by creating a voxel layer model, we cannot identify and analyze further the existing seismic data, this requires other software that is more detailed related to seismic data. #geoscience #geology #gis #mining #oilandgas

Confirmation of Predictions: How confident are we that our machine learning predictions of the presence or absence of oil are an accurate reflection of the reality of the subsurface. Drilling the prospects is expensive and with a new and emerging technology, it is a tough sell....... However, what we can do is compare our predictions with known geologic conditions to see if the predictions match the known geologic conditions. The Austin Chalk is a good candidate for this. In Medina County, Texas, oil production ceases to the north due the Chalk outcropping and increases to the south as it dives into the basin. What we found was that this contact can be predicted using open-source remote sensing data in a bagging algorithm. I've documented the results in a short video. Using machine learning in oil exploration is a new way new way to look at the subsurface. https://lnkd.in/grn7iPNM Thanks again to Troy Tittlemier for the lead. Keep up the good work. Embrace the Laser and See the Fingerprints of the Earth. Chuck

🌍 The ever-increasing global demand for energy challenges us to maximize recovery from existing fields. Achieving this relies heavily on 𝘢𝘤𝘤𝘶𝘳𝘢𝘵𝘦 𝘳𝘦𝘴𝘦𝘳𝘷𝘰𝘪𝘳 𝘮𝘰𝘥𝘦𝘭𝘴, and one of the most precise tools for subsurface data collection is 𝘣𝘰𝘳𝘦𝘩𝘰𝘭𝘦 𝘪𝘮𝘢𝘨𝘪𝘯𝘨. 🎥 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠: "Borehole Image Log Technology: Application Across the Exploration and Production Life Cycle" ✨ 𝐖𝐡𝐲 𝐁𝐨𝐫𝐞𝐡𝐨𝐥𝐞 𝐈𝐦𝐚𝐠𝐢𝐧𝐠? Borehole imaging captures 𝐦𝐢𝐥𝐥𝐢𝐦𝐞𝐭𝐞𝐫-𝐬𝐜𝐚𝐥𝐞 𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐝𝐚𝐭𝐚, offering real-time integration into reservoir models during drilling and significantly optimizing operations. 🔬 𝐑𝐞𝐜𝐞𝐧𝐭 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬: 𝐇𝐢𝐠𝐡-𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐨𝐢𝐥-𝐛𝐚𝐬𝐞𝐝 𝐢𝐦𝐚𝐠𝐞𝐫𝐬 and LWD tools rivaling wireline resolution. Enhanced geosteering capabilities through advanced geomechanical analyses. Evolution from simple dipmeters to state-of-the-art tools like the 𝐅𝐮𝐥𝐥𝐛𝐨𝐫𝐞 𝐅𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐌𝐢𝐜𝐫𝐨𝐈𝐦𝐚𝐠𝐞𝐫 (𝐅𝐌𝐈) over the past 40 years. 📌 This video will guide you through how borehole imaging is applied at each stage of the 𝐞𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐥𝐢𝐟𝐞 𝐜𝐲𝐜𝐥𝐞, from job planning to complex field development projects. 🚀 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐈𝐧𝐜𝐥𝐮𝐝𝐞: 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Borehole imaging reduces reservoir risk and enhances structural predictions, even in hazardous environments. 𝐀𝐩𝐩𝐫𝐚𝐢𝐬𝐚𝐥: Calibrated image logs improve structural and stratigraphic interpretations, optimizing well design and reducing costs. 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Image logs enhance well placement strategies by analyzing reservoir connectivity and characterizing faults. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: Integrated imaging and petrophysical data refine reservoir models for efficient reservoir management. 🔮 𝐅𝐮𝐭𝐮𝐫𝐞 𝐎𝐮𝐭𝐥𝐨𝐨𝐤: The next generation of imaging tools and software will prioritize: Improved resolution and broader coverage. Enhanced geomechanical modules for seamless data integration. Transitioning imaging from 𝐪𝐮𝐚𝐥𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐭𝐨 𝐪𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐭𝐢𝐨𝐧𝐬, enabling more precise subsurface analysis. 💡 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐊𝐞𝐲: Acquiring high-quality image logs requires close coordination between operators and service providers. Proper planning, quality control, and data integration are essential for maximizing the value of these technologies. 📺 𝐖𝐚𝐭𝐜𝐡 𝐧𝐨𝐰 𝐭𝐨 𝐮𝐧𝐜𝐨𝐯𝐞𝐫 𝐡𝐨𝐰 𝐛𝐨𝐫𝐞𝐡𝐨𝐥𝐞 𝐢𝐦𝐚𝐠𝐢𝐧𝐠 𝐢𝐬 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐫𝐞𝐬𝐞𝐫𝐯𝐨𝐢𝐫 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐚𝐧𝐝 𝐟𝐢𝐞𝐥𝐝 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧! #EnergyTransition #BoreholeImaging #ReservoirModeling #Geomechanics #OilAndGasInnovation #SubsurfaceData #4DGeosteering