Socio-Technical Changes for Sustainable Rice Production: Rice Husk Amendment, Conservation Irrigation, and System Changes

Rice is a staple food and primary source of calories for much of the world. However, rice can be a dietary source of toxic metal(loid)s to humans, and its cultivation creates atmospheric greenhouse gas emissions and requires high water use. Because rice production consumes a significant amount of natural resources and is a large part of the global agricultural economy, increasing its sustainability could have substantial societal benefits. There are opportunities for more sustainable field production through a combination of silicon (Si) management and conservation irrigation practices. As a Si-rich soil amendment, rice husks can limit arsenic and cadmium uptake, while also providing plant vigor in drier soil conditions. Thus, husk addition and conservation irrigation may be more effective to attenuate the accumulation of toxic metal(loid)s, manage water usage and lower climate impacts when implemented together than when either is implemented separately. This modified field production system would take advantage of rice husks, which are an underutilized by-product of milled rice that is widely available near rice farm sites, and have ~10% Si content. Husk application could, alongside alternate wetting and drying or furrow irrigation management, help resolve multiple sustainability challenges in rice production: (1) limit arsenic and cadmium accumulation in rice; (2) minimize greenhouse gas emissions from rice production; (3) decrease irrigation water use; (4) improve nutrient use efficiency; (5) utilize a waste product of rice processing; and (6) maintain plant-accessible soil Si levels. This review presents the scientific basis for a shift in rice production practices and considers complementary rice breeding efforts. It then examines socio-technical considerations for how such a shift in production practices could be implemented by farmers and millers together and may bring rice production closer to a bio-circular economy. This paper's purpose is to advocate for a changed rice production method for consideration by community stakeholders, including producers, millers, breeders, extension specialists, supply chain organizations, and consumers, while highlighting remaining research and implementation questions.

Understanding and Modeling Gas Breakthrough in the Karachaganak Gas Condensate Field

Field production constrained with surface facilities on gas handling have to deal with well rates optimization by reducing gas oil ratio of the field production. This means the best way of reducing gas oil ratio on field level is not by closing wells with the highest gas oil ratio but chocking back wells where gas breakthrough occurred and GOR of a well is rate dependent [1]. In this paper, authors modeled and analyzed wells with gas breakthrough in single porosity and dual porosity sector models. The analysis showed single porosity models underestimate severity of gas breakthrough and fail to predict rate dependent GOR of a well in the field. Also, based on the sector model using machine-learning technique an empirical equation was developed to estimate rate dependent GOR of a well which can be further used in field level production optimization exercise to reach maximum liquid production under gas processing constraints.

Production Optimization in Mature Field Through Scenario Prediction Using a Representative Network Model: A Rapid Solution Without Well Intervention

As part of the production optimization exercise in J field, an initiative has been taken to enhance the field production target without well intervention. J field is a mature field; the wells are mostly gas lifted, and currently it is in production decline mode. As part of this optimization exercise, a network model with multiple platforms was updated with the surface systems (separator, compressors, pumps, FPSO) and pipelines in place to understand the actual pressure drop across the system. Modelling and calibration of the well and network model was done for the entire field, and the calibrated model was used for the production optimization exercise. A representative model updated with the current operating conditions is the key for the field production and asset management. In this exercise, a multiphase flow simulator for wells and pipelines has been utilized. A total of ∼50 wells (inclusive of idle wells) has been included in the network model. Basically, the exercise started by updating the single-well model using latest well test data. During the calibration at well level, several steps were taken, such as evaluation of historical production, reservoir pressure, and well intervention. This will provide a better idea on the fine-tuning parameters. Upon completion of calibrating well models, the next level was calibration of network model at the platform level by matching against the platform operating conditions (platform production rates, separator/pipeline pressure). The last stage was performing field network model calibration to match the overall field performance. During the platform stage calibration, some parameters such as pipeline ID, horizontal flow correlation, friction factor, and holdup factor were fine-tuned to match the platform level operating conditions. Most of the wells in J field have been calibrated by meeting the success criterion, which is within +/-5% for the production rates. However, there were some challenges in matching several wells due to well test data validity especially wells located on remote platform where there is no dedicated test separator as well as the impact of gas breakthrough, which may interfere to performance of wells. These wells were decided to be retested in the following month. As for the platform level matching, five platforms were matched within +/-10% against the reported production rates. During the evaluation, it was observed there were some uncertainties in the reported water and gas rates (platform level vs. well test data). This is something that can be looked into for a better measurement in the future. By this observation, it was suggested to select Platform 1 with the most reliable test data as well as the platform rate for the optimization process and qualifying for the field trial. Nevertheless, with the representative network model, two scenarios, reducing separator pressure at platform level and gas lift optimization by an optimal gas lift rate allocation, were performed. The model predicts that a separator pressure reduction of 30 psi in Platform 1 has a potential gain of ∼300 BOPD, which is aligned with the field results. Apart from that, there was also a potential savings in gas by utilizing the predicted allocated gas lift injection rate.