Agriculture accounts for approximately 10% of global greenhouse gas emissions and is simultaneously associated with impacts on human health through food consumption, and agricultural air pollutant emissions. These impacts are often quantified separately, and there is a lack of modelling tools to facilitate integrated assessments. This work presents a new model that integrates assessment of agricultural systems on (i) human health indirectly through dietary, obesity and malnutrition health risks from food consumption, (ii) human health directly through exposure to air pollutants from agricultural emissions, and (iii) greenhouse gas emissions. In the model, national food demand is the starting point from which the livestock and crop production systems that meet this are represented. The model is applied for 2014–2018 to assess the robustness of the GHG emissions and health burden results that this integrated modelling framework produces compared to previous studies that have quantified these variables independently. Methane and nitrous oxide emissions globally in 2018 were estimated to be 129 and 4.4 million tonnes, respectively, consistent with previous estimates. Agricultural systems were also estimated to emit 44 million tonnes of ammonia. An estimated 4.1 million deaths were associated with dietary health risks, 6.0 million with overweight/obesity, and 730 thousand infant deaths from malnutrition, consistent with previous studies. Agricultural air pollutant emissions were estimated to be associated with 537 thousand premature deaths attributable to fine particulate matter (PM2.5) exposure, and 184 thousand premature deaths from methane-induced ground-level ozone. These health impacts provide substantial opportunities to design integrated strategies that mitigate climate change, and improve human health, and also highlight possible trade-offs that the expansion of agricultural production could have due to increased emissions. The model presented here provides for the consistent evaluation of the implications of different agricultural strategies to meet food demand while minimising human health and climate change impacts.

Reservoir surveillance and production optimization will remain at the forefront of company strategies in the new post-COVID19 environment. We anticipate that companies will focus more on producing assets and go the route of production enhancement rather than exploration. Accordingly, production logging will remain an important surveillance method in evaluating and strategizing production-optimization schemes pertaining to flow-characterization from reservoir-to-wellbore. This work is culmination of operational and technical excellence that enabled the revival of a loaded-up well through simultaneous lifting-and-logging technique. Conventionally, wireline is the preferred mode of conveyance for production-logging; however, well must be continuously flowing throughout acquisition timeframe. Kicking-off the well using nitrogen-lift and then bringing in wireline-unit for production-logging in Well A-4 was not feasible as previous attempts confirmed well to load-up in few hours post-offloading. Therefore, success of this project was heavily dependent on initial planning stage, which accounted for all available data including production-history, well-events, intervention-details, fluid analysis and well load-up behavior. Next, a multi-domain approach was adopted while bringing-out each domain from its silos and strategize collectively to simultaneously kickoff the well with nitrogen and acquire real-time downhole production-logging data through smart-coiled-tubing (CT). This was first implementation of concurrent lifting and logging operation in Pakistan. By deploying the approach mentioned above through smart CT (using optical-telemetry-link inside the CT-string coupled with downhole-assembly), synchronized lifting-and-logging operation was carried-out successfully. Well was observed to swiftly go back to load-up conditions post-kickoff; however, continuous well dynamics monitoring downhole enabled us to log perforated interval across multiple time domains. Well was activated through CT nitrogen-injection but depicted continuous loading tendency, which was captured downhole in form of flow-transients. Real-time job optimization ensured vigilant monitoring and selection of right-time to acquire meaningful zonal-contribution data for evaluation and diagnostic solutions. Finally, operational excellence was complemented through technical data analysis and interpretation, integrating passes data with transients and stationary measurements. Ultimately, acquired data analyzed using an integrated lens involving fluid velocities, downhole density, temperature, and water hold up data. Consequently, enabling us to decipher gas and water-entries on a zonal-basis across perforated sandstone reservoir.

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