Using Machine Learning to Predict Lost Circulation in the Rumaila Field, Iraq

Abstract

Lost circulation costs are a significant expense in drilling oil and gas wells. Drilling anywhere in the Rumaila field, one the world's largest oilfields, requires penetrating the Dammam formation, which is notorious for lost circulation issues and thus a great source of information on lost circulation events. This paper presents a new, more precise model to predict lost circulation volumes, ECD and ROP in the Dammam formation. A larger data set, more systematic statistical approach, and a machine learning algorithm have produced statistical models that give a better prediction of the lost circulation volumes, ECD, and ROP than the previous models for events. This paper presents the new model, validates the key elements impacting lost circulation in the Dammam formation, and compares the predicted outcomes to those from the older model. The work previously in the literature provided a platform for predicting the severity of lost circulation incidents in the Dammam formation. Using the new models, the predictions closely track actual field incidents of lost circulation. When new lost circulation events were compared with predictions from the old and new models, the new model presented a much tighter prediction of events. Three equations for optimizing operations were developed from these models focusing on the elements that have the highest degree of impact. The total flow area of the nozzles was determined to be a significant factor in the ROP model indicating that nozzle size should be chosen carefully to achieve optimal ROP. Good modeling of projected lost circulation events can assist in evaluating the effectiveness of new treatments for lost circulation. The Dammam formation is a significant source of lost circulation in a major oilfield and warrants evaluation of the effectiveness of lost circulation treatments. These techniques can be applied to other fields and formations to better understand the economic impact of lost circulation and evaluate the effectiveness of various lost circulation mitigation efforts.

Artificial neural network models to predict lost circulation in natural and induced fractures

“…Lost circulation estimation is a limited topic in the literature; only a few papers were published about this topic. Some shortcomings were identified in the previous work as follows [34,36,[39][40][41][42]]:…”

“…The data went through processing steps where all outliers, errors, white spaces were removed [43]. Input data were selected based on previous statistical and sensitivity analysis studies conducted that showed the most influential parameters on mud loss as well as experts' opinions [39,41]. Table 2 shows the parameters used to create the models and a summary of statistics for both induced and natural fractures.…”

Research on Prediction Model of Fracture Width in Loss Formation Based on Artificial Neural Network

“…Abbas [10] established a loss rate prediction model based on artificial neural network, analyzed the relationship between loss rate and drilling parameters, and controlled drilling parameters can effectively prevent drilling fluid loss. Al-Hameedi [11] conducted in-depth statistical analysis of more than 500 wells in the Rumaila oilfield, proposed a model for predicting loss in the Dammam formation, and proposed a model for optimizing drilling operations. Sabah [12] collected a large amount of data from 61 wells recently drilled in Iran's Marun oilfield, and established artificial neural network, decision tree, adaptive neural-fuzzy inference system, and genetic algorithm-multilayer perception model to quantitatively predict lost circulation.…”

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

“…Loss of circulation increases the nonproductive time spent on mitigating the losses [6], besides increasing the total drilling cost due to loss of drilling mud, which represents, in some cases, 40% of the total cost. The oil and gas industry reported more than $12 billion in the cost of drilling materials and fluids in 2018 [7]. Loss of circulation leads to poor hole cleaning due to the reduction of mud level in the borehole, which decreases its ability to transfer the cutting outside the wellbore [8].…”