Halliburton Energy Services, Inc.

Rama Subba Reddy Thavva

Chaitanya Mallikarjun Karale

Harold Grayson Walters

Dwight David Fulton

Manisha Bhardwaj

Ankit Bhatnagar

Ajish Sreeni Radhakrishnan Potty

Richa Chauhan

Vikesh Singh Baghel

The disclosure is directed to methods to design and revise hydraulic fracturing (HF) job plans. The methods can utilize one or more data sources from public, proprietary, confidential, and historical sources. The methods can build mathematical, statistical, machine learning, neural network, and deep learning models to predict production outcomes based on the data source inputs. In some aspects, the data sources are processed, quality checked, and combined into composite data sources. In some aspects, ensemble modeling techniques can be applied to combine multiple data sources and multiple models. In some aspects, response features can be utilized as data inputs into the modeling process. In some aspects, time-series extracted features can be utilized as data inputs into the modeling process. In some aspects, the methods can be used to build a HF job plan prior to the start of work at a well site. In other aspects, the methods can be used to revise an existing HF job plan in real-time, such as after a treatment cycle, a pumping stage, or a time interval.

Hydraulic fracturing operation planning using data-driven multi-variate statistical machine learning modeling

Methods, systems, and computer program products for generating a schedule for fluid placement. A computer-implemented method may include determining, for each zone of a plurality of zones of a treatment interval to be treated by the fluids placement system, a criterion value based on a heterogeneity parameter of the zone, sorting the zones into an ordered sequence based on the criterion value associated with each zone, and generating a schedule that designates an order of treating the zones in accordance with the order of the sequence.

Scheduling treatment fluid placement and fluid diversion in a subterranean formation

Methods and systems for detection of crossflow and quantification of crossflow effects on fluid distribution during multistage injection treatments of hydrocarbon bearing reservoir formations. A low rate injection stage is applied after a main (high rate) fluid injection stage of a multistage injection treatment process. Wellbore temperature measurements can be utilized along with an inversion process to detect any crossflow and quantify its effects on fluid distribution during the main injection stage. After the effects of any detected crossflow on the fluid distribution are quantified, the effectiveness of the multistage injection treatment can be analyzed and any adjustments or modifications to the treatment can be made accordingly.

Quantification of crossflow effects on fluid distribution during matrix injection treatments

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Pune, India

Rama Subba Reddy Thavva