Abstract

Production chemicals are essential to the operation of oil and gas production systems – but chemicals such as scale inhibitors are complex, with different chemistries providing protection for different types of situations, depending on system conditions. To date, the standard industry process has depended on experience, laboratory testing and field trials.

We are introducing a first of-its kind method of scale inhibitor selection which takes advantage of digital capabilities, rich performance data and our deep chemicals expertise to eliminate much – and sometimes all – of the laboratory work.

In specific, this process eliminates the time needed for short-listing and testing scale inhibitor chemistries in the laboratory. We have instead applied digital analytics to our extensive historical data library, making it possible to recommend the appropriate chemistry with reduced – or even completely without – laboratory testing. Comparing system conditions to historical laboratory data, we can generate relevance factors that relate to the conditions of the system of interest. If sufficient information is available in the database, the analytics can also predict the results that would be expected in a given laboratory test. In addition, data on where each scale inhibitor has been applied is stored in the database, giving a link to field scenarios and case histories, thereby correlating laboratory and field performance.

Laboratory tests confirm that the use of this new digital application results in much quicker (same day) and more accurate product recommendations, with statistical qualification. It allows the user to generate faster recommendations, optimize dosages, generate tailored products, and avoid delays from shipping samples around the world. Ultimately, it provides more statistically relevant information on which to base field decisions, minimizing risk for the operator, as well as much quicker and optimal product recommendations.

We believe that this revolutionary scale inhibitor chemical selection tool is the first of its kind to incorporate such an extensive database of historical test data with digital correlation to existing field use information. We believe it is the future for designing the optimum scale treatment program for oil and gas fields.

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