Shinji Yoneshima’s Post

AI Well Planner now has the capability to design an entire well pad. You input the surface coordinates, target points, kick-off point, DLS and inclination constraints for the TVD intervals, ESP requirements, as well as anti-collision rules. Then, you run the calculation, wait, and evaluate the results. Additionally, AI Well Planner can do the same, but with cost optimization for well construction. To do this, you need to specify the Wellbore Geometry and the cost of various components such as casing, cement, drilling mud, drilling time, etc. And the cherry on top is the target vs wellhead sorting algorithm. The optimal matching of targets to wellheads significantly simplifies collision avoidance, makes drilling safer, and life easier 😀 Find out more at https://mwdstd.com/ #ai #ml #machinelearning #drilling #drillingengineering

AI Well Planner took 3 minutes to design a trajectory that saved over $700,000 while accounting for torque-and-drag for casing running, kick-off point, and DLS constraints. You probably wouldn't have had time to make a coffee...   We analyzed well construction costs affected by the trajectory, and selected the following factors: drilling cost per meter, drilling time, drilling fluid, casing, cement slurry, and cuttings disposal. We also considered that not every geometrically precise trajectory is suitable for running casing - AI Well Planner now takes into account torque-and-drag during the casing running process.   Redesigning the transport section from the previous post saved $745,000 compared to a design by human. The snapshot load graph also shows that the casing of the third section is at the limit of helical buckling. This is an extreme case, demonstrating AI Well Planner's ability to find the most optimal solution. In reality, a safety factor of 0.7-0.8 will be used for helical buckling. #ai #ml #machinelearning #drilling #drillingengineering #oilandgas

AI tools must be tools to help us and our engineers to do a better job, to save money and to give us time to life balance. Actually we have with our engineers to develop and improve our models, the data is te most important value to AI and machine learning

STUCK PIPE INCIDENT. A stuck pipe incident occurs in drilling operations when the drillstring becomes immobilized or difficult to move further down the wellbore. This can happen due to various reasons, including differential sticking, mechanical obstructions, wellbore instability, or equipment failure. When a pipe becomes stuck, it can result in costly downtime, compromised wellbore integrity, and potential safety hazards. Artificial intelligence (AI) can play a significant role in mitigating and addressing stuck pipe incidents by leveraging advanced data analytics, predictive modeling, and real-time decision support. Here's how AI can treat this issue: -Early Detection: AI algorithms can continuously analyze drilling parameters, surface data, and downhole measurements to detect early signs of a potential stuck pipe incident. By identifying deviations from expected trends or anomalies in data patterns, AI systems can alert drilling personnel to take preventive action before the situation escalates. -Root Cause Analysis: AI-powered diagnostic tools can conduct root cause analysis to determine the underlying factors contributing to a stuck pipe incident. By analyzing historical data, geological formations, drilling practices, and equipment performance, AI can identify the primary causes and help operators understand why the incident occurred. -Decision Support: AI platforms can provide real-time decision support to drilling engineers and rig crews when addressing a stuck pipe situation. By analyzing vast amounts of data and considering various factors such as formation properties, drilling parameters, and operational constraints, AI systems can recommend optimal courses of action to free the stuck pipe safely and efficiently. -Predictive Maintenance: AI-based predictive maintenance models can anticipate potential equipment failures or malfunctions that may lead to a stuck pipe incident. By monitoring equipment health, vibration patterns, and performance metrics in real time, AI systems can identify early warning signs of impending issues and enable proactive maintenance interventions to prevent downtime. -Dynamic Wellbore Modeling: AI-powered wellbore modeling software can simulate different scenarios and predict the behavior of the wellbore under various conditions. By incorporating geomechanical data, fluid dynamics, and drilling parameters, AI models can optimize drilling strategies and mitigate the risk of wellbore instability or formation damage that could contribute to a stuck pipe incident. -Continuous Learning and Improvement: AI systems can continuously learn from past stuck pipe incidents and their outcomes to improve their predictive capabilities and decision-making algorithms. By analyzing historical data and performance feedback. A relevant video below. Contact :[email protected] #AI #drilling #virtualassistant #stuckpipe #incident #enginereering

While you're discussing over lunch why the markets and oil prices have dropped, AI Well Planner can design an entire well pad for you, accounting for anti-collision, torque-and-drag for casing running, kick-off point, and DLS constraints. So, you'll still have time to figure out where the markets will end up... And yes, the well pad can be redesigned every time a finely planned trajectory clashes with the harsh realities of directional drilling. Perhaps... Although all of this might become irrelevant if the oil price drops below $30 per barrel. What do you think? #ai #ml #machinelearning #drilling #drillingengineering #oilandgas

Last week Fabio Concina, represented Kwantis at the SPE A.I.4Energy Workshop hosted by SPE Aberdeen! 💡Fabio’s talk introduced an innovative anomaly detection pipeline for surface logging data, which integrates traditional and AI techniques to detect low-quality intervals and enhance data integrity in drilling operations. For more insights check the full article: https://lnkd.in/dqm8Uxxr #anomalydetection #drillingdata #AI #realtimedata #drillingoperations #qaqc #id3 software

As we know a stool needs three legs to stand - at #AIQ we deliver revolutionary #ai #solutions with three key ingredients: #datascientists #swengineers and #SMEs …..supported by volumes of #data

What is synthetic seismic modeling used for? Being president of Tesseral Ai and supporting our products I need to explain this. BTW, there are many in Tesseral Ai who support and help me with some of this.

𝗦𝗲𝗶𝘀𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 1. **Bandpass Filtering:** Bandpass filtering selectively removes frequency components outside a specified range while retaining frequencies of interest. This technique is commonly used to suppress low-frequency noise (such as ground roll) and high-frequency noise (such as air blasts) while preserving seismic reflections. 2. **Denoising Algorithms:** Denoising algorithms, including median filtering, wavelet denoising, and empirical mode decomposition, aim to attenuate random and coherent noise present in seismic data. These algorithms exploit the statistical properties of noise and signal to enhance signal-to-noise ratio (SNR) and improve data quality. 3. **Spectral Whitening:** Spectral whitening techniques aim to flatten the seismic spectrum by equalizing the amplitude of frequency components across the seismic bandwidth. This approach enhances the resolution of seismic data and improves the interpretation of subtle geological features. 4. **Deconvolution:** Deconvolution techniques, such as predictive deconvolution and wavelet deconvolution, aim to enhance seismic resolution by compensating for the effects of the seismic wavelet and source signature. Deconvolution removes reverberations and enhances the definition of reflectors, particularly in complex geological settings. 5. **Adaptive Filtering:** Adaptive filtering algorithms, such as the least-mean-squares (LMS) algorithm and the recursive least squares (RLS) algorithm, adaptively adjust filter coefficients based on the local characteristics of the seismic data. Adaptive filtering is particularly effective for attenuating coherent noise and enhancing signal fidelity. ### Significance in Subsurface Imaging: 1. **Improved Interpretation:** Filtered seismic data provide clearer and more coherent images of subsurface structures, facilitating accurate interpretation of geological features, stratigraphy, and fault systems. 2. **Enhanced Reservoir Characterization:** By suppressing noise and enhancing signal resolution, filtering techniques enable geoscientists to delineate reservoir boundaries, identify fluid contacts, and estimate reservoir properties with greater confidence. 3. **Reduction of Imaging Artifacts:** Filtering techniques mitigate common imaging artifacts, such as multiples, ground roll, and acquisition footprint, which can obscure seismic reflections and complicate interpretation efforts. Photo refrence, credit : https://lnkd.in/df2bnKQs