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Big Data-Powered Machine Learning for Operational Excellence: A Case Study Analysis for Oil & Gas

Abstract


Drilling and workover operations represent a crucial part of a well lifecycle in terms of deliverability and

economics. Understanding the underlying phenomena that cause operational anomalies is the stepping

stone into early detection and control of undesired events, such as a kick.


This paper highlights a novel machine learning model developed to streamline early detection for the operational anomaly of uncontrolled hydrocarbon flow during well operations such as drilling.

The evolution of artificial intelligence and machine learning applications lend itself to well operations to

gain new efficiencies and unveil hidden insightful observations about downhole and surface operating

conditions. Incorporating the mechanisms of natural phenomena and big data, retrieved from sources

such as logging while drilling (LWD) and measurement while drilling (MWD), into machine learning

models boost capabilities for early detection of operational anomalies and mitigation of potential

negative consequences, while eliminating human-bias.


This paper highlights a novel machine learning model developed to streamline early detection for the

operational anomaly of uncontrolled hydrocarbon flow during well operations such as drilling. The

proposed technique detects and classifies the risk level of a kick much before it reaches the surface to

extend the safe response time limit. When this method is integrated with LWD data in real-time mode

by means of a software, and alarm system can be embedded to alert field hands about downhole

conditions. This does not only promote safer operations, but also significantly improve the availability

and reliability of critical information.


To further fine-tune the accuracy of the predictive model, multiple rounds of cross-validation were executed on the training data. It is evident that training machine learning models allows for more

learning through practice. The technique presented shows that big data and machine learning

algorithms are powerful tools to uncover hidden information and enable continuum leap improvement

in operational leadership.


Introductory Background


The daily flow of vast, fast and varied data represents vast opportunities for the oil and gas industry as

this data is processed and refined into meaningful information (Baaziz, 2014). Accordingly, machine

learning models are used to analyze the collected data to generate insights that would describe, predict

and prescribe operating trends. Other advantages of machine learning are elimination of human-bias

and streamlining error-prone repetitive tasks. Machine learning models are categorized into four major

categories: unsupervised learning, supervised learning, semi-supervised learning and reinforcement

learning (Noshi and Schubert, 2018).


One machine learning application that is gaining wider applicability is anomaly detection during drilling,

workover and completion operations. Many companies find it appealing to use machine learning algorithms to identify undesired outcomes, such as kicks and blowout, in real-time mode (Al-Ghazal,

2018). According to Unrau and Torrione (2017), anomaly detection machine learning algorithms offer

new capabilities, such as reduction in false alarm reports, higher anomaly detection rating and reduction

in detection time lag.


Influx of fluids into wellbore is an operational anomaly encountered during a drilling operation, even

though the flow of fluids at surface is controlled using methods, including change in mud weight and

shutting well. A severe case of fluid influx is uncontrolled flow of hydrocarbons from a high pressure

subsurface structure is termed as blowout. The Foundation for Scientific and Industrial Research at

Norwegian Institute of Technology (SINTEF) conducted a study and found that about 117 well control

incidents occurred in the period from 2000 through 2014. Given the importance of safety while drilling a

well, a relatively limited volume of literature is published in the area of kick detection and management

using data-driven models and machine learning algorithms.


Fraser et al. (2014) suggests a method to early detection of influx flow rate for offshore wells. Installing a

Coriolos Flowmeter outside of the riser at the seabed gives the ability to measure flowrate even before

the fluids reach the surface, reducing kick detection volume by a factor of 2. Unrau et al. (2017)

developed a machine learning algorithm to prevent false alarms of fluid influx. Fluctuations in flow rate

and mud levels due to operations, including changing mud pump rates, making connections and

transferring mud, were accounted for and the system was trained to accurately detect fluid influx or

mud loss.


Predictive Data Analytics Model Development


In this study, the operational anomaly of hydrocarbon kick is being detected using parameters recorded

through logging while drilling (LWD) and measurements while drilling (MWD). At the most fundamental

level, this novel approach considers the following conditions for a well in order to experience a kick:


  1. Subsurface rock is porous enough to hold any fluid.

  2. Subsurface rock pores contain fluid.

  3. The actual subsurface pressure is higher than expected pressure (calculated based on pressure gradient data).


Using LWD tool data, it is possible to determine if the above three conditions are met in real-time. Kicks

are conventionally identified by a rise in fluid level at the mud pits. The proposed method will detect a

kick before it reaches the surface and alarm the crew ahead of time to be prepared for necessary well

control measures including to eliminate or abate any potential negative consequences. LWD tool

assembly parameters required for this study are as follows: Resistivity Log, Neutron Porosity Log, Bulk

Density Log and Pressure Measurement (Dowell et al., 2006).


Considering each of the three aforestated conditions separately and expanding on how it can be

achieved using LWD data is useful in developing the proposed predictive machine learning model. First,

subsurface rock should be porous enough to hold any fluid. Combination of Bulk density and Neutron

Porosity logs can determine the porosity of the subsurface formation. Figure 1 illustrates typical bulk density and neutron porosity logs. The positive separation of the two logs indicates a porous formation

with fluids. Second, subsurface rock pores should have presence of fluids. Resistivity logs can investigate the presence of fluids in the shallow, near to wellbore and deep regions of the hole. In this study, we consider any spike (in case of hydrocarbons) or drop (in case of brine) in resistivity measurement for an extended depth interval with a minimum 15 ft. This is to compensate for noise in measurements attributed to presence of fluid. This is illustrated in Figure 1.


In this study, we consider any spike (in case of hydrocarbons) or drop (in case of brine) in resistivity measurement for an extended depth interval with a minimum 15 ft. This is to compensate for noise in measurements attributed to presence of fluid.
Fig. 1. Typical bulk density and neutron porosity logs used for detecting the presence of hydrocarbons and type of fluid.

Any unexpected rise in pressure gradient can be attributed to influx of fluids. However, noise in the data could indicate a false kick. To mitigate such false indications, pressure gradient is calculated over 30 ft interval (Δpsi/ 30ft), even though the tool may measure pressure every 10 ft. If the recorded pressure gradient is higher than the expected pressure gradient by 30psi/30ft (green line on Figure 2), fluid influx is assumed, Figure 2.
Fig. 2. Pressure and pressure gradient vs. depth (Appendix).

Third, the actual subsurface pressure should be higher than expected pressure. Assuming downhole

pressure measurement tool is part of LWD and MWD assembly, the instrument measures downhole

pressure every 10 ft and the pressure gradient is calculated (Δpsi/10ft) and recorded every 10 ft. Any

unexpected rise in pressure gradient can be attributed to influx of fluids. However, noise in the data

could indicate a false kick. To mitigate such false indications, pressure gradient is calculated over 30 ft

interval (Δpsi/ 30ft), even though the tool may measure pressure every 10 ft. If the recorded pressure

gradient is higher than the expected pressure gradient by 30psi/30ft (green line on Figure 2), fluid influx

is assumed, Figure 2. Expected pressure gradient can be calculated knowing the depth of the well, hole

geometry, mud properties and volume of annular hydrostatic column.


This method can identify a kick just 30 ft after the pressure measurement tool records the pressure at a

given depth. Unlike traditional detection methods that rely on surface flow parameters and visual

inspection of mud pits volumes for kick identification, the proposed predictive method identifies a kick

much before the downhole influx reaches the surface. When this method is integrated with LWD data in

real-time by means of a software, an alarm system can be developed to notify the rig crew about

possibility of fluid influx. This ensures safer drilling operations and assists the crew to be prepared for

any well control activities. This kick identification method requires minimal cost of installation (only the

cost of alarm system) when a comprehensive LWD assembly is already installed.


A decision matrix, as shown in Table 1, is designed to indicate the risk of kick based on the data

recorded. Knowledge of the typical measurements of different logs and pressure gradients for a

particular oilfield is helpful to assign risk classifications of high, medium or low based on recorded

parameters’ value range.

A decision matrix, as shown in Table 1, is designed to indicate the risk of kick based on the data recorded. Knowledge of the typical measurements of different logs and pressure gradients for a particular oilfield is helpful to assign risk classifications of high, medium or low based on recorded parameters’ value range.
Table 1. A decision matrix for risk classification of high, medium, or low.

Model Optimization


The novel machine learning technique, introduced in this paper, eliminates the time lag, often

encountered to detect and classify undesired events, utilizing big data retrieved in real-time mode from

logging while drilling and measurement while drilling. A pattern recognition logic, which is fit to existing

operating workflow frames, is used to analyze the LWD and MWD data and identify symptoms of

downhole formation fluid kick and its corresponding risk level.


The input parameters for the data-driven machine learning model are LWD and MWD data, including

resistivity, neutron porosity, bulk density and pressure gradient. These inputs (represented as vector

matrices) are the features and observations used to understand the fluid kick. The target variable of the

model is fluid influx classification (Yes or No influx), which is a discrete data set. The data science

classification decision tree method was selected to categorize the target variable because it is suitable

for discrete set of values, which is the case in this application. The ultimate purpose of the classification

decision tree method is to split the dataset into a class of either influx or no influx.


As far as model training to fine-tune accuracy and reliability, MWD and LWD data was used from

multiple wells to feed and calibrate the model. The trained model was further tested using multi-fold

cross validation test method. Additionally, multiple rounds of cross-validation were executed to

streamline the predictive data analytics model.


It is evident that machine learning model training allows the algorithm model to learn through repetitive

practices. This work is a testimony to the power of big data and machine learning algorithms to unveil

insights about drilling and completion operations that would enhance decision-making to safeguard

human lives and valuable assets.


Summary


Digitalization, in the form of artificial intelligence, machine learning and big data analytics, continues to

progress in the oil and gas industry, spanning upstream, midstream and downstream applications. In the

specific case of this paper, a novel machine learning model for early detection of the operational

anomaly of kick has been outlined. The machine learning model was developed to streamline early

detection of a kick to enable new control capabilities towards operational robustness, which is the need

of the hour to add non-existing efficiencies.


The model uses a decision matrix to categorize the risk of kick based on the data recorded from LWD

and MWD. The input parameters for the predictive machine learning model are LWD and MWD data,

including resistivity, neutron porosity, bulk density and pressure gradient. These inputs are the features

and observations used to describe the downhole conditions. The target variable of the model is fluid

influx identification and classification. The data science classification decision tree method was selected

to categorize the target variable because it is suitable for discrete set of values, which is the case in this

application. The ultimate purpose of the classification decision tree method is to split the dataset into a

class of either influx or no influx based on pre-determined value ranges. Finally, multiple rounds of

cross-validation were executed to verify the predictive data analytics model and its accuracy.


References


  1. Al-Ghazal, M.A. “The Value of Digital Data Analytics,” Oil & Gas Vision 14: 10-11, 2018.

  2. Baaziz, A. “How to Use Big Data Technologies to Optimize Operations in Upstream Petroleum Industry,” 21 st World Petroleum Congress, 15-1 June 2014, Moscow, Russia.

  3. Fraser, D., Lindley, R., Moore, D. D., & Vander Staak, M. “Early Kick Detection Methods and Technologies,” SPE 170756-MS presented at the SPE Annual Technical Conference and Exhibition, 27-29 October 2014, Amsterdam, The Netherlands.

  4. Dowell, I., Mills, A., Ridgway, M. and Lora, M. (2006). Petroleum Engineering Handbook. (Volume II). Society of Petroleum Engineers. 647-685.

  5. Noshi, C. I. and Schubert, J. J. “The Role of Machine Learning in Drilling Operations; A Review,” SPE 191823-18ERM-MS presented at the SPE Eastern Regional Meeting, 7-11 October 2018, Pittsburgh, Pennsylvania, USA.

  6. Unrau, S. and Torrione, P. “Adaptive Real-Time Machine Learning-Based Alarm System for Influx and Loss Detection,” SPE 187155-MS presented at the SPE Annual Technical Conference and Exhibition, 9-11 October 2017, San Antonio, Texas, USA.

  7. Unrau, S., Torrione, P., Hibbard, M., Smith, R., Olesen, L., & Watson, J. “Machine Learning Algorithms Applied to Detection of Well Control Events,” SPE 188104-MS presented at the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, 24-27 April 2017, Dammam, Saudi Arabia.

Pressure gradient calculation example
Appendix 1. Pressure gradient calculation example data set.

Technical Paper authored by: Mohammed A. Al-Ghazal and Viranchi Vedpathak


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