sentence-transformers How to use Sampath1987/mpnet-energy with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Sampath1987/mpnet-energy")
sentences = [
"How does the monitoring system for well integrity function after CO2 injection?",
"Drilling is a complex process and delivering a successful well requires identifying proper technologies and utilizing them efficiently to save time & cost. Today in Oil & Gas industry there is a huge focus on digital technologies to improve Drilling Process efficiency and PDO decided to implement an innovative approach of process optimization by implementing a unique project \"electronically Delivering the Limit (eDtL)\".\nThe overall approach with eDtL project was to implement a platform which can provide Drilling Operations team the technical limit for all Drilling Activities, which is the theoretical minimum time required to perform an activity, based on available knowledge and technology.\neDtL system utilizes rig sensors data transmitted in Real-Time from Drilling Rigs to automatically detect the Rig Activity and focus on identifying the areas of Drilling Performance Improvements and minimizing redundant tasks for rig and office teams. The identified opportunities are communicated with rig team for implementation and the performance is tracked again to highlight the improvements.\neDtL system also provides capability for continuous improvement of organizational processes by introducing automation of redundant tasks. One of such improvement was partial automation of Daily Drilling Report which was historically manually recorded by rig team daily.",
"ADNOC has embarked on a major Carbon Capture and Storage (CCS) project where large quantities of CO2 are injected into deep saline aquifers for permanent storage instead of releasing into the atmosphere.\nAn advanced chemical tracer technology was deployed in the first CCS project in the UAE for continuous CO2 monitoring to ensure permanent and safe CO2 storage. In case of containment breach, the chemical tracer technology can confirm the leakage and identify its source.\nAfter CO2 injection for permanent storage, any containment breaching would be detected in the shallow soil monitoring borehole. Few soil monitoring boreholes were excavated across the field in which Capillary Adsorption Tubes (CAT) were inserted for some time and replaced by another according to the sampling frequency plan. The tube is sent to the lab for CO2 leak detection and reporting. The high detection resolution is in the order of 0.1 parts per trillion (ppt). This has a positive impact on the system economics because smaller quantities of chemical tracer material are required.\nThe tracer injection monitoring system is ongoing in the first CO2 storage area of Abu Dhabi. The monitoring includes soil monitoring which are shallow boreholes. The soil monitoring boreholes were excavated close to the CO2 injection well to ensure that there are no well integrity issues developed due to thermal effects by CO2 injection. The soil monitoring boreholes to be verified by surface gas CO2 monitors. Soil monitors were located around the radial storage area, to detect CO2 leakage and to understand CO2 migration to the soil through the cap rock (in case of leakage). The monitoring system for caprock and well integrity will provide: Surface soil monitoring for cap rock integrity, integrity confirmation for legacy wells, integrity confirmation of injection well in the post-injection monitoring period, leakage quantification, leakage origin if multiple injectors. The monitoring system can continue for up to 30 years of the operational period as well as the full post-injection monitoring, measurement and verification horizon.\nThis paper presents a description of a sophisticated CO2 monitoring technology that is being deployed in UAE's first CCS project. CO2 tracer technology is considered as one of the most accurate methods to detect CO2 leakage at surface. Its high-detection resolution allows early leakage identification and early mitigation action. In addition, it proves to be relatively low cost, operationally easy to execute, and requires a small operational footprint.",
"Carbon Capture and Storage, as a solution to mitigate the increase in greenhouse gases emissions in the atmosphere, is still bringing intensive worldwide R&D activities. In particular, significant acceleration of in situ CCS experiments supports technical developments as well as acceptability of this technology. Among the major risks identified to this technology, wells are often considered to be the weakest spots with respect to CO2 confinement in the geological reservoir. Therefore, long-term well integrity performance assessment is one of the critical steps that must be addressed before large scale CCS technology deployment is accepted as a safe solution to reduce CO2 emissions.\nA risk-based methodology associated with well integrity is proposed within CO2 geological storage. The main objectives of this approach are to identify and quantify risks associated with CO2 leakages along wells over time (from tens to thousands of years), to evaluate risks and to propose relevant actions to reduce unacceptable risks. The methodological framework emphasized the use of the risk concept as a relevant criterion to (i) evaluate the overall performance of well confinement with respect to different stakes, (ii) include different levels of uncertainty associated to the studied system, and (iii) provide a reliable decision making support. For the quantification of risk, a coupled CO2 flow model (gas flow and degradation processes) was used to identify possible leakage pathways along the wellbore and quantify possible CO2 leakage towards sensitive targets (surface, fresh water, any aquifers…) for different scenarios. This approach offers an operational response to some of the challenges inherent to well integrity management over well lifecycle.\nThis paper focuses on the application of the methodology to a synthetic case based on an existing well. The practical outcomes and the added values will be presented: (i) an objective and structured process, (ii) scenarios identification and quantification of CO2 migration along the wellbore for each scenario, (iii) risk mapping, (iv) and operational action plans for risk treatment of well integrity."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
