ai-assisted decision-making workflow for oilfield water management

Numerous issues plague water availability in iran, including low annual rainfall, poor rainfall and surface water distribution patterns and climate change. in the past 20 years, iran has lost over 200 cubic kilometers of its stored water, while groundwater levels have also dropped rapidly at an average rate of 28 cm per year. while poor management of irrigation practices with saline water, lack of suitable drainage infrastructure, large volumes of high salinity water co-produced with oil and discharge of drainage water to river systems are common human-induced salinization practices in the past three decades. production data-driven diagnostics in combination with artificial intelligence (ai) and machine learning (ml) can be utilized for intelligent and integrated decision making on water shut off candidate selection to prevent oilfield water excess production. in this paper, we utilized production data, single well geomechanial modeling and artificial intelligence and ml assisted decision-making process for oilfield water control. the potential of image processing and ai to assist oilfield geologists in inspecting cbl vdl logs is investigated in this work. image processing techniques can be used to enhance the visibility of features in the logs, and ai algorithms can be trained to identify and classify different types of features in the logs, such as rock types, fractures, and bed boundaries. this can help geologists to more quickly and accurately interpret the logs, which can aid in the exploration and production of oil and gas resources.