Mud loss estimation using machine learning approach

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, equivalent circulation density (ECD), and rate of penetration (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 presented by Al-Hameedi et al. (http://www.onepe tro.org, 2017a; http://www.AADE.org, 2017b) 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.

Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations

“…Although physics-based models can be used to avoid LCIs, data-driven models offer another layer of information that can be used to estimate or predict unseen events based on historical data from offset drilling operations as well as real-time data collected during drilling operations. In this direction, different machine learning (ML) and deep learning (DL) models have been developed to predict mud loss of circulation from surface parameters [21][22][23][24]. Moazeeni et al [25] reported one of the earliest studies utilizing ML to develop a model capable of predicting LCIs in different areas of a specific oilfield, as well as to estimate the quantity and quality of LCIs.…”

“…We applied MAF twice for each feature with ∈ [24,48] records, which correspond to [2,4] minutes, respectively, in the most common data frequency of 0.2 Hz. This allows for two levels of macroscopic trends and also two denoising stages, which the ML/DL model can learn from during training.…”

“…However, the noise type and frequency associated with drilling operations differ from one operation to another and from one sensory equipment to another. Whereas this type of noise is often random and timevariant, we tackled the noise frequency issue by engineering multiple GF features with different standard deviations, namely ∈ [3,24,48]. Overall, GF is useful in alleviating data inaccuracy, particularly noise, and local outliers.…”

“…We also parameterized this feature with , the distance or timesteps between differentiated records. In this application, we used multiple ∈ [1,24]. This family of features is important to time-series analysis as it provides a sense of change with respect to time, which alleviates the multiple data sources issue of variant frequencies across operations and sensory equipment.…”

Experimental investigation of bio-enhancer drilling fluid additive: Can palm tree leaves be utilized as a supportive eco-friendly additive in water-based drilling fluid system?

“…Drilling fluid is very important in the drilling operation. If the drilling fluid was not designed properly, many unwanted consequences can happen such as lost circulation and stuck pipe issues (Alkinani et al 2019;Al-Hameedi et al 2018). To obtain the desired properties, chemical and non-biodegradable materials have been the traditional additives that are used in drilling fluids.…”