Abstract Problems related to gas hydrate formation abound in oil field operations due to deep offshore activities, leading to severe adverse effects and safety problems all of which add to operating cost (OPEX). Prevention of these problems is key. The use THIs and KHIs have been explored but has their disadvantages. Due to the alarming rate of environmental degradation via toxic substances, there is an urgent need for friendly alternatives for both humans and the environment. This study compares the use of Brassicales Extract (BPE) and Poly n-vinyl caprolactam (N-VCap) as gas hydrate inhibitors. The BPE was evaluated using a locally fabricated stainless pipe mini flow loop. Hydrate formation and inhibition experiments were carried out for a period of 120 minutes with pressure and temperature readings recorded every 2 minutes. A rapid decrease in pressure and sudden increase in temperature are pointers to hydrate formation in the constant volume batch process loop. Microsoft excel was used to generate plots relevant to hydrate formation and inhibition. The various pressure plots showed that the BPE performed better than the conventional in most weight percentages considered. Pressure decline and temperature increase was more regulated in the systems with BPE unlike in the uninhibited experiment. The Volume of gas used up in the BPE inhibited system were 23.3, 14.7, 16.7, 15.3 and 12.7% for 0.01-0.05wt% respectively while for N-VCap, the volume of gas used up for the same weight percentage were 24, 17.3,22, 10.7 and 29.3% at the end of the experiments. BPE has inhibition efficiency values of 69.3, 80.7, 78.07, 79.82 and 83.3% for 0.01-0.05wt% respectively while that of the synthetic inhibitor was 68.42, 77.2, 71.1, 86 and 61.4%. It is recommended that the BPE be developed for gas hydrate inhibition because it did very well and is environmentally friendly unlike the N-VCap which is a toxicant. 

Inhibition Effect of Order Brassicales Plant Extract (BPE) on Methane Gas Hydrate Formation in a Gas Dominated System


 Casein peptone efficacy as a hydrate inhibitor is investigated, as is its potential for synergy with two eminent kinetic hydration inhibitors (KHIs), polyvinylcaprolactam (PVCap) and polyvinylpyrrolidone (PVP), to improve hydrate inhibition performance. The induction time (IT) / hydrate formation delay time and the average hydrate growth rate (AHGR) post-hydrate formation at concentrations of 0.25 wt%, 0.5 wt%, and 1 wt% of casein peptone were evaluated for methane gas at 7.6 MPa, with a hydrate equilibrium temperature of 10.45°C. 1°C/h constant cooling rate is used in the hydrate formation tests.
 The individual effectiveness of casein peptone, PVP, and PVCap is evaluated. The synergy between common KHIs (PVP and PVCap) and casein peptone is assessed by creating 1:1 blends of casein peptone with PVP and PVCap, with the individual performance of PVP and PVCap at an equivalent total inhibitor concentration (KHI + synergist and KHI) for a fair comparison.
 Casein peptone is found to have a promising hydrate inhibiting performance in the study. At similar concentrations, it outperformed the conventional KHI PVP40K, demonstrated similar efficacy to the conventional KHI PVP10K, and exhibited only slightly lower performance than the conventional high performing KHI PVCap 40K. This finding highlights the potential of casein peptone as a viable alternative to traditional KHIs, particularly for offshore locations where environmental concerns are a priority. The ability of casein peptone to inhibit hydrate formation at levels comparable to existing conventional inhibitors, coupled with its natural origin and biodegradable properties, make it an attractive candidate for future research in the field of deep-sea hydrate risk management.

Using Casein Peptone as a Sustainable and Natural Solution for Mitigating Gas Hydrate-Induced Flow Assurance Challenges in Natural Gas Pipelines


 Hydrocarbon production optimization is essential in pursuing the best scenarios for economic outcomes. But because of complex and multi-dimensional nature of production processes, thousands of scenarios are possible. Extensive data collection may allow uncovering patterns still unidentified. With on-site computing power increasing, cloud availability, and artificial intelligence evolution, mathematical optimization methods are becoming powerful and accessible. Data type-tailored models are implemented for history matching and prediction of operational efficiency of the asset. This paper presents a comprehensive analysis and comparison of three data type-tailored reservoir modeling methods and their optimization process for waterflooding field cases.
 The mathematical techniques used were Data-Driven Capacitance Resistance Model (CRM), Numerical Simulators (Data-Physics) coupled to Smart Algorithms Optimizers, and Hybrid Model (Machine Learning Physics-Based). They were compared to 1-identify the benefits of mathematical optimization techniques, 2-illustrate the methods developed to sort out time and computing capacity restrictions, and 3-validate the techniques by comparing the forecast with actual results.
 The six study cases of different reservoir types in Argentina, Venezuela, and the USA, had different types data availability. Four had no static model. In two cases, field results were available to confirm the accuracy of the forecasted injection and production. The forecasted increase in Net Present Value (NPV) and cumulative oil production (Np) ranged to 30%, and optimized water injection rates decreased by 50%.
 Traditional modeling techniques yielding unreliable result in one field with hundreds of producing layers and unknown lateral and vertical continuity were solved using a machine learning technique. In some cases, they pointed toward non-intuitive infill drilling sequence and injection water redistribution. Also, they pointed to options that reduce economic risk. The methods yielded many better economic scenarios and increased the flexibility of operationalizing plans. In one field requiring excessive computing power, using time horizons reduction and successive year-by-year optimization yielded 4 times the NPV of the base case. This approach solves objections related to long computing time and system instability. With the three mathematical techniques, the asset value could be continually maximized by a novel implementation of a heuristic decision-making approach that continuously challenge the current scenario. It makes a systematic formulation of conceivable new scenarios, competing through an objective function determining the probity of compared scenarios.
 The optimization also resulted in an up to 50% decrease in water injection requirements and the same percentual CO2 emissions reduction.

Comparative Analysis of Data-Driven, Physics-Based and Hybrid Reservoir Modeling Approaches in Waterflooding


 The synergy between a plant-based polysaccharide k-carrageenan (k-crgn and two well-known kinetic hydrate inhibitors (KHIs), polyvinyl caprolactam (PVCap) and polyvinylpyrrolidone (PVP), is investigated, to achieve a higher hydrate inhibition performance. The effectiveness of the hydrate inhibition is assessed by measuring the induction time (IT) required for hydrate formation and by the growth rate of the hydrate plugs after nucleation. Hydrate formation tests are conducted using a constant cooling rate of 1°C/h. Experimental results of PVP and PVCap at 0.5 wt% are compared with solutions of 0.25 wt% KHIs (PVP or PVCap) blended with 0.25 wt% k-crgn to keep the equal total inhibitor concentration for a fair comparison. Results indicated that the blended mixtures (k-crgn + KHI) have higher induction times by 20–35% and up to 90% lower hydrate growth rates when compared to the IT and growth rates of PVP and PVCap. This suggests k-crgn to be an excellent hydrate inhibitor synergist to PVP and PVCap and possibly to the other commercial hydrate inhibitors.

Synergistic Hydrate Inhibition by Red Seaweed Extract K-Carrageenan With Kinetic Hydrate Inhibitors


 As the oil and gas industry embarks on the path to energy transition, pressure from government regulators, investors, and the public in general demand that companies have clear and transparent net-zero goals and that their operational initiatives and plans support such transition efforts. Mature fields present an opportunity to increase production through operational optimization, which at the same time, can also lead to greenhouse gas (GHG) emissions efficiency.
 This paper presents the application of a novel modeling and optimization technique in a mature waterflood environment. Data Physics is the amalgamation of the state-of-the-art in machine learning and the same underlying physics present in reservoir simulators. These models can be created as efficiently as machine learning models, integrate all kinds of data, and can be evaluated orders of magnitude faster than full scale simulation models, and since they include similar underlying physics as simulators, they have good long term predictive capacity and can even be used to predict performance of new wells without any historical data. The technology was applied to a mature field in the Neuquen basin in Argentina to effectively reduce the amount of water injected into the reservoir with no negative impact on the production. Additionally, a new Carbon Intensity (CI) modeling tool was used to compare the emissions intensity before and after optimization showing a significant improvement in CI achieving three objectives in one single decision: 1) obtain significant water injection reduction with its corresponding impact in injection and water treatment costs; 2) maintaining production compared to the initial decline of the field, improving the top line; and 3) improving the GHG emissions intensity hence the long term benefit to the environment.
 The paper deals more with the implementation of the technologies than the technologies themselves, assuming that readers unfamiliar with both Data Physics and Carbon Intensity tools will refer to the references section to gain familiarity with these.

Optimizing a Waterflood Using a Combination of Machine Learning and Reservoir Physics. A Field Application for Reducing Fresh Water Injection with no Impact on Oil Production and Improved Carbon Intensity

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Synergistic Hydrate Inhibition by Red Seaweed Extract K-Carrageenan With Kinetic Hydrate Inhibitors

Optimizing a Waterflood Using a Combination of Machine Learning and Reservoir Physics. A Field Application for Reducing Fresh Water Injection with no Impact on Oil Production and Improved Carbon Intensity