Introducing SpeedWise® Waterflood Management (SWM) - the game-changing cloud-native SaaS solution for modern asset teams. By combining cutting-edge subsurface flow physics with advanced data-driven techniques, SWM allows asset teams to optimize waterflood operations like never before. With SWM, users can build waterflood models quickly and accurately, utilizing available geologic, completion, and production data. Improve capital efficiency and reduce OPEX in mature waterfloods.
SpeedWise Waterflood Management (SWM) is a revolutionary cloud-native SaaS solution for asset teams looking to optimize waterflood operations. SWM combines the fundamentals of subsurface flow physics with advanced data-driven techniques to quickly build waterflood models based on available geologic, completion, and production data. Utilizing this streamlined approach saves hours of computational power on unrelated properties, allowing the user to focus solely on the waterflood problem.
The flexibility of SWM aids daily production operations drastically with fast production forecasts and optimization of existing wells and new drills. This improvement in capital efficiency and OPEX reduction is significant in mature waterfloods, enabling users to reach their desired levels of success more efficiently than ever before. SpeedWise Waterflood Management is truly an invaluable addition to any asset team toolkit.
AWS Marketplace price reflects the platform price only and does not include training, migration and additional services.
Highlights
Patented reduced-physics, data-driven approach for fast and accurate reservoir modeling.
Physics models include gravity and compressibility effects, fractured and faulted reservoirs, and proprietary optimization algorithms for automated history-matching and performance optimization.
Scalable cloud architecture for real-time model update and production forecast and optimization.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
This listing offers one contract option: SWM Enterprise, priced per user. You commit to a set number of users under a single plan. Each user allotment includes a fixed resource bundle: 0.5TB of storage, 150 jobs per month, and 75 cloud compute hours per month. Pricing scales with the number of users you enroll rather than by separate tiers or add-ons. The cloud-native platform runs on AWS infrastructure and supports waterflood modeling and optimization. All included resources reset or apply on a monthly basis within the contract term.
Top-of-mind questions for buyers
What counts as one user for billing under the SWM Enterprise plan?
One user is one individual account that can access projects, define or view cases, and collaborate on results. Each user you enroll adds their own resource bundle of 0.5TB storage, 150 jobs per month, and 75 cloud compute hours per month. Cost scales with the number of enrolled users.
What happens if I exceed 150 jobs or 75 cloud compute hours in a month?
Each user's bundle allots 150 jobs and 75 cloud compute hours per month. These are fixed monthly allotments tied to each enrolled user. If your usage regularly nears these limits, adding more users raises your total pooled allotment. For specific overage handling, contact the vendor.
Which resource in the bundle drives how many users I should enroll?
Each user carries the same fixed bundle: 0.5TB storage, 150 jobs monthly, and 75 cloud compute hours monthly. All three scale together per user. Estimate your enrollment by whichever resource you consume fastest — heavy modeling favors compute hours and jobs, while large datasets favor storage capacity.
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