Advancement in Data Engineering and Feature Processing Workflow by Using Deep Learning Techniques for the Automation of ESP Failure Root Cause Analyses

Today, Electrical Submersible Pump (ESP) failure analysis is a tedious, human-intensive, and time-consuming activity involving dismantle, inspection, and failure analysis (DIFA) for each failure. This paper presents a novel artificial intelligence workflow using an ensemble of machine learning (ML) algorithms coupled with natural language processing (NLP) and deep learning (DL). The algorithms outlined in this paper bring together structured and unstructured data across equipment, production, operations, and failure reports to automate root cause identification and analysis post breakdown. This process will result in reduced turnaround time (TAT) and human effort thus drastically improving process efficiency.

Quadrant Mapping Artificial Lift Concept

This paper outlines a concept for monitoring performance of artificial lift performance such as electrical submersible pump (ESP), hydraulic pumping unit (HPU), sucker rod pump (SRP) and progressive cavity pump (PCP), for a large number of wells. The objective is to generate simplified monitoring performance of artificial lift with a huge number of wells on one page by creating quadrant mapping consisting of two coordinates with x axis representing pump efficiency and y axis showing pump submergence. We made a four-quadrant limit by pump efficiency (50%) and submergence (200 m). Optimum wells will show on range pump efficiency above 50% and submergence below 200 m, and 3 other quadrants are classified as artificial lift problems, well potential and sizing/design problems. By using the quadrant mapping concept, we can generate performance of artificial lift for 1500++ wells in one page, and this mapping consists of four quadrants (quadrant 1, quadrant 2, quadrant 3 and quadrant 4), quadrant 1 (Submergence above 200 meter and lifting efficiency below 50%) showing wells which have artificial lift problem, quadrant 2 (Submergence is above 200 meters and efficiency is above 50%) showing well which have potential to increased production, quadrant 3 (Submergence is below 200 meters and efficiency is above 50%) showing the optimum wells operation and quadrant 4 (Submergence is below 200 meters and efficiency is below 50%) showing the wells which required to re-sizing/re-design artificial lift. This quadrant mapping can be shown to Engineers, manager's and shareholder to show overall performance and classification detailed problems to create a troubleshooting, optimization program to increased oil production, run life artificial and result in better production performance. This mapping also helps petroleum engineers to get a better field view and create priorities and program optimization based on the quadrant mapping result and classification.

Submersible Pump with Line Shaft Pump in Shallow Water, Optimum Selection for Offshore Application in the Oil & Gas Industry

Seawater is an essential fluid used in various process circuits, such as cooling, reinjection into the wells and utilities, etc., in the offshore oil and gas industry. Vertical pumps facilitates with lifting seawater to the platform. This study investigates and compares two pump alternatives that has been widely used in oil and gas industry for seawater lift application: Vertical line-shaft pump and Electrical submersible pump. Existing seawater lift pump operating parameters are used as the basis of this study. The pump that is considered for the study has a flow rate of 3415 US GPM (776 m3/hr.) with a total head of 250 ft. The motor rating is 350 HP. The overall length of the pump is 21 meters. The main methodology used is a Life Cycle Cost Analysis (LCC) where the total cost of ownership of the vertical line-shaft pump and electrical submersible pump were analyzed for a period of 30 years. Furthermore, this research also addresses the operational drawbacks associated with both the pumps. Submersible pumps have higher initial capital investment cost when compared to line-shaft pump of similar capacity and size. The energy consumption cost of submersible pumps are higher mainly owing to lower efficiencies of the motors. The power factor for submersible pumps are lower in relation to line- shaft pumps. One of the main benefits of submersible pumps are their less installation and pump pullout time. Submersible pumps occupies lower space above ground when compared to line-shaft pumps. Additionally, submersible pumps are less noisy and have lower vibration in comparison to line-shaft pumps. This paper aims to provide key information and knowledge for engineers to make prudent decision regarding selection of the most cost effective pump for the seawater lift application with a tangible added cost value to both Capital Expenditures (CAPEX) and Operational Expenditure (OPEX).

First Retrieval and Redeployment of a Novel Rigless Electrical Submersible Pump System Via Coiled Tubing in Kuwait: A Story of Success

With the continuous production from Kuwait oil reservoirs, a clear decline in reservoir pressure is observed. Subsequently, the demand for artificial lift is increasing to sustain production. Maintenance of those wells requires frequent interventions and continuous presence of workover rigs, which affects overall cost of production. Change of the electrical submersible pump (ESP) deployment method represents one of the cost reduction initiatives undertaken by the operator to reduce well intervention time and improve asset utilization. To minimize deferred production generated by the ESP replacement operation, a novel rigless approach leveraging coiled tubing (CT) was introduced in southeast and west Kuwait. It reduces operating costs and eliminates disruptions to operations by enabling rigless retrieval and redeployment of a standard ESP assembly. To evaluate the efficiency of using CT as rigless ESP retrieval and conveyance method, two candidate wells were selected to recover and redeploy a 108-ft-long ESP system. The intervention methodology relied on CT equipped with optical line and real-time downhole telemetry, a high-pressure rotary jetting tool, and a specific ESP deployment assembly. The retrieval and redeployment of the ESP was executed in a single rigless intervention, averaging less than 72 hours of operational time per well. This represents five times improvement over the standard practice using a workover rig. The intervention was executed in several stages, according to the well intervention program, and included tubing drift and cleanout runs, retrieval, inspection, and redress of the ESP assembly, followed by its successful redeployment. The high-pressure rotary jetting tool was used to condition the wellbore tubulars across the fishing area, while downhole real-time data enabled by the 1 3/4-in. CT equipped with optical telemetry were instrumental to eliminate uncertainties associated with changing downhole conditions. The casing collar locator allowed live depth control and ensured accurate positioning of the ESP. Its careful retrieval and redeployment were monitored thanks to the downhole axial force readings, which allowed controlling the weight applied on the fishing assembly. Internal and external downhole pressure data, along with downhole temperature, helped in controlling actuation and use of the high-pressure rotary jetting nozzle under nominal conditions for maximum efficiency. This enhanced rigless ESP replacement technique, made possible by the joint use of CT and real-time downhole measurements, was confirmed as a robust workover method for retrieval and redeployment of rigless ESPs in southeast and west Kuwait. The experience gained in the first two wells brings a new level of confidence to Kuwait operators about this technique, which certainly can be expanded to other fields in the Middle East and elsewhere.

New correlations for predicting two-phase electrical submersible pump performance under downhole conditions using field data

Electrical Submersible Pump (ESP) is one of the major Artificial Lift methods that is reliable and effective for pumping high volume of fluids from wellbores. However, ESP is not recommended for applications with high gas liquid ratio. The presence of free gas inside the pump causes pump performance degradation which may lead to problems or even failure during operations. Thus, it is important to investigate effect of free gas on ESP performance under downhole conditions. At present, existing models or correlations are based on/verified with experimental data. This study is one of the first attempts to develop correlations for predicting two-phase gas–liquid pump performance under downhole conditions by using field data and laboratory data. Field data from three oil producing wells provided by Strata Production Company and Perdure Petroleum LLC. as well as experimental data obtained from experimental facility at Production and Drilling Research Project—New Mexico Tech were used in this study. Actual two-phase pump differential pressure per stage is obtained from experiments or estimated from field data and was normalized using pump performance curve. The values are compared to pump performance curve to study the relationships between pump performance and free gas percentage at pump intake. Correlations to predict ESP performance in two-phase flow under downhole and experimental conditions was derived from the results using regression technique. The correlation developed from field data presented in this study can be used to predict two-phase ESP performance under downhole conditions and under high gas fraction. The results from the experimental data confirm the reliability of the developed correlation using field data to predict two-phase ESP performance under downhole conditions. The developed correlation using the laboratory data predicts quite well the two-phase pump performance at the gas fraction of less than 15% while it is no longer reliable when free gas fraction is more than 15%. The findings from this study will help operating companies as well as ESP manufacturers to operate ESPs within the recommended range under downhole conditions. However, it is recommended to use the proposed correlation on reservoirs with conditions similar to those of the three presented wells.

Hybrid Electrical-Submersible-Pump/Gas-Lift Application to Improve Heavy Oil Production: From System Design to Field Optimization

The typical challenge encountered in developing heavy-oil reservoirs is inefficient wellbore lifting caused by complex multiphase flows. The literature on modeling of a hybrid artificial lift (AL) system is relatively sparse and these works typically model the AL system on the basis of individual AL methods. This paper presents a case study of the design and optimization of a hybrid AL system to improve heavy-oil production. We systematically design and model a hybrid electrical-submersible-pump/gas-lift (ESP/GL) system to enhance wellbore lifting and production optimization. We found that the implementation of hybrid ESP/GL system provides the flexibility to boost production and reduces production downtime. Results from the pilot test show that the production rate in hybrid mode is approximately 30% higher than in ESP-only mode. The power consumption of the hybrid mode is 3% lower in the ESP-only mode. Furthermore, the average ESP service life exceeds six years which is better than expected in the field development plan. The pump-performance-curve model is built with corrections for density and viscosity owing to the increased water production. We observed a higher pressure drawdown with GL injection at fixed ESP frequency. The GL injection reduces the density of the fluid column above the ESP, resulting in less pressure loss across the pump, less power consumption, and potentially extended service life. The nodal-analysis results suggest that the pump capacity can be considerably expanded by manipulating the GL rate instead of increasing the frequency.

Implementing the autonomous adaptive algorithm to manage ESP operation in harsh reservoir conditions

The well geometries with a shallow kick-off point in conjunction with surface infrastructure limitations have led to Electrical Submersible Pump (ESP) technologies' application as one of the most suitable artificial lift methods for the harsh reservoir conditions. However, the harsh reservoir conditions in terms of the low reservoir pressure, high reservoir temperature, scaling problems in various forms, and high gas content at the pump intake have reduced the ESP system run life. Therefore, this research represents the Autonomous Adaptive Algorithm (A3) as a holistic approach to integrate analytical and machine learning models to assist production engineers in the early detection of operating problems. The A3 relies on different data sources and uses unique, well diagnostics logic to generate valuable features and prepare data for training. Finally, the paper evaluates different classifiers and explores the possibilities of application A3 as a flexible edge solution. The research benefits will be demonstrated for several problematic ESP wells.