Saudi Arabian Oil Company

Nasser Mubarak Al-Hajri

Mohammed Dhafer Al-Ajmi

Abdulaziz Essam AlDughaither

Ibrahim A Al-Obaidi

Adib A Al-Mumen

Mohamed M El Nekhily

Fahad Muhammed Al-Meshal

Ibraheem M Al-Ageel

Abdullah Abdulaziz AlGhamdi

To control hydrostatic backpressure of disposal wells connected to a disposal plant surface network, density of water to be injected from a water disposal plant system into disposal wells formed in a subterranean zone, a density of a hydrostatic backpressure-modifying additive to modify a density of the water, a target total injection flow rate of the water, and a vertical depth of a portion of the subterranean zone through which the water is to be injected are identified. Injected water flow rate upstream of an injection point into the multiple disposal wells, wellhead injection pressure needed to achieve the target total injection flow rate and a total injection rate are periodically received. Based on these parameters, dosage rate of the additive to maintain the target total injection flow rate is periodically determined. An additive quantity is injected into the water and periodically modified based on the periodically determined dosage rate.

Chemical hydrostatic backpressure reduction methodology to flow fluids through disposal wells

Methods for prediction and inhibition of calcium carbonate scale in hydrocarbon wells using machine learning include extracting training data including parameters from aqueous samples. Each aqueous sample is collected from a respective hydrocarbon well. The training data is classified in accordance with hydrocarbon production conditions of each hydrocarbon well. The classified training data is labeled in accordance with whether calcium carbonate scale has formed in each aqueous sample within a particular time period. A feature vector is determined from the labeled training data based on the parameters extracted from each aqueous sample. The feature vector is indicative of whether the respective hydrocarbon well contains calcium carbonate scale. A trained machine learning model is generated, wherein the machine learning model is trained based on the feature vector, to predict a number of the hydrocarbon wells containing calcium carbonate scale within the particular time period.

Calcium carbonate scale prediction and inhibition in hydrocarbon wells using machine learning

Techniques to determine wellbore leak crossflow rate between formations in an injection well are described. The techniques repurpose well performance principles to achieve the objective of cross flow rate quantification without the need to run a flowmeter.

Determining wellbore leak crossflow rate between formations in an injection well

Provided are systems and method for operating and maintaining downhole inflow control valves (ICVs) of hydrocarbon wells, including monitoring the operations of ICVs to identify failure and non-failure times of defective and non-defective ICVs, respectively, determining a probability of failure of the ICVs (P) based on the failure and non-failure times, determining an operational stroking duration based on the probability of failure of the ICVs (P), and operating ICVs in accordance with the operational stroking duration determined.

Systems and methods for operating downhole inflow control valves

One example of a wellbore control tool and a method of use is described. A leak is detected in a cased wellbore comprising a multiple telescoping casing sections. A fluid flows in an uphole direction through the cased wellbore. A flow of the fluid in the uphole direction results in a first force in the uphole direction. The leak is in a first telescoping casing section. An open downhole end of a wellbore tool having a weight at least equal to a second force greater than the first force on an uphole end of a second telescoping casing section that is downhole from a location of the leak in the first telescoping casing section is seated. The open downhole end of the wellbore tool provides metal to metal contact against the uphole end of the second telescoping casing section and restricts fluid flow into the leak.

Well control using a modified liner tie-back

Some examples of determining wellbore leak crossflow rate between formations in an injection well are described. During normal operation of an injection well, multiple bottomhole pressures are determined based on multiple surface injection pressures. An Inflow Performance Relationship (IPR) for the injection well is determined based on the multiple bottomhole pressures and the multiple injection flow rates. After shut-in responsive to a subsurface leak, the shut-in injection well is modeled as a producing well having the IPR determined during normal operation of the injection well. A crossflow rate in the injection well at a location of the subsurface leak in the injection well is determined based on the IPR of the modeled producing well.

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Abqaiq, Saudi Arabia

Nasser Mubarak Al-Hajri