Application of Advanced Data Analytics for Gas Reservoirs and Wells Management

2021 ◽  
Author(s):  
Ashwin Srinivasan ◽  
Gaurav Modi ◽  
Rahul Agrawal ◽  
Viren Kumar
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Production Management ◽  
Petroleum Industry ◽  
Oil Field ◽  
Petroleum Engineering ◽  
Multiple Sources ◽  
Opportunity Identification ◽  
Diagnostic Plots ◽  
Time Required

Abstract Objectives/Scope The amount of time and effort required to access and integrate Subsurface data from multiple sources is significant. Using Advanced Data Analytics, mainly python, an integrated subsurface dashboard titled Hybrid Integrated Visualization Environment (HIVE) was created using Spotfire to empower the integrated Exploration, Development and Well Reservoir and Facilities Management (WRFM) subsurface teams in: Professionalizing data and knowledge management to have "one" version of the truth. Data consolidation and preparation to avoid repetitive manual work & Enhancing opportunity identification to optimize production and value Methods, procedure, process The approach of subsurface data integration can be broken down into 4 major steps, namely: Step 1: Python programming was used to pre-process, restructure and create unified data frames. Use of python significantly reduces the time required to pre-process a diverse number of subsurface data sources consisting of static, dynamic reservoir models, log data, historical production & pressure data and wells & completion data to name a few. Step 2: - Standard diagnostic industry recognized diagnostic plots were automated using advanced analytic techniques in HIVE with the help of unified data frames. Step 3: HIVE was created to link various internal corporate data stores like pressure, temperature, rate data from PI System (stores real time measured data), Energy Components (EC) and Oil Field Manager (OFM) in real time. This was done to ensure that data from various petroleum engineering disciplines could now be visualized and analyzed in a structured manner to make integrated business decisions. Step 4: One of the key objectives of pursuing this initiative was to ensure that subsurface professionals in Shell Trinidad and Tobago were trained and upskilled in the use of python as well visualization tools like Spotfire and Power BI to ensure the maintenance and improvement of HIVE going forward. Results, Observations, Conclusions The development of HIVE has made it easier and more efficient to access and visualize subsurface data, which was extremely time consuming earlier while using older conventional techniques. Standard diagnostic plots and visuals were developed and are now used to drive integrated decision making, with key focus being water and sand production management from a production management perspective. Consequently, HIVE also drives enhanced integration between disciplines (Petrophysics, Petroleum Geology, Production Technology, Reservoir Engineering and Production operations) and departments (Developments, Upstream and Exploration). Novel/Additive Information The petroleum industry has started to embrace the application of advanced data analytics in our day-to-day work. A successful application of these techniques results in transforming the ways of working by increasing efficiency, transparency and integration among teams.

Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics

2021 ◽  
Author(s):  
Gaurav Modi ◽  
Manu Ujjwal ◽  
Srungeer Simha
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Optimization Technique ◽  
Material Balance ◽  
Oil Production ◽  
Data Driven ◽  
Petroleum Engineering ◽  
Short Term ◽  
Injection Water ◽  
Facility Level

Abstract Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.

Collaborative Working Environment CWE Strategy: An Enterprise Approach to Operational Excellence

2021 ◽  
Author(s):  
ElFadl Z. Ibrahim ◽  
Mariam A. Al Hendi ◽  
Abdulla Al-Qamzi ◽  
Nasser A. Ballaith ◽  
Maha A. Al Naqbi ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Petroleum Industry ◽  
Oil Field ◽  
Working Environment ◽  
Strategic Objective ◽  
Collaborative Working ◽  
Day To Day Operations ◽  
Set Up ◽  
Operational Issues

Abstract Collaborative Working Environments (CWE) are a business solution that improve the quality and speed of decision making by enriching the collaboration between teams and individuals, which results in tangible business benefits. The advantages of working in a collaborative environment are well understood in the organization and the concept is widely embraced throughout the petroleum industry. CWEs provide seamless communication between disciplines and between teams in different locations. Traditionally, they have been used to connect staff in remote locations to teams in the headquarters, allowing real time monitoring of the health of the field, and fast decision making on operational issues and short to medium term optimization opportunities. The main goal is to be quickly alerted to events and make smarter, faster decisions using key capabilities available to the company with access to all relevant knowledge, data and analytical tools required to reach a decision. But this drive to make smarter, faster decisions is applicable to all levels of a company. In fact, it becomes increasingly important as more complex decisions are required at higher levels, which can be influenced by interpreted data, personal opinions and perceptions. In line with strategic objective of digital transformation, a national oil company (NOC) has extensive plans to develop asset specific CWEs and enterprise level CWEs. These will be centralized collaboration facilities to provide more rigorous, effective, and consistent surveillance & optimization to help reduce deferment costs and inefficiencies and accelerate decision-making with a measurable business value to enhance HSE, Reservoir, Drilling, Well and Production system performance through emerging digital innovation. All these centers shall be equipped to receive real time and episodic data and perform exception-based surveillance through trending, analysis, and condition diagnosis. All these CWE Centers shall enable decision making with efficient multi-disciplinary collaboration to address business challenges and increase the efficiency of day-to-day operations. They will have clear roles and responsibilities serving as an integral element of the value realization across the assets. The paper will describe the enterprise CWE strategy, key technical considerations, methodology and standards that have been set up to achieve the ultimate objective of the organization to maximize oil field recovery, eliminating non-productive time, enhancing HSE aspects and increasing profitability through the deployment of these various centers.

Definition of an Offshore Petroleum Production System by Using Fuzzy Sets and Utility Functions

2005 ◽  
Vol 9 (6) ◽  
pp. 684-692 ◽  
Author(s):  
Tiago C. da Fonseca ◽  
◽  
José R. P. Mendes ◽  
Celso K. Morooka ◽  
Ivan R. Guilherme ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Production System ◽  
Petroleum Industry ◽  
Utility Functions ◽  
Oil Field ◽  
Petroleum Engineering ◽  
Decision Making Process ◽  
Field Development ◽  
Definition Of

Field development is a very important task in the petroleum industry. Decisions in this area may lead either to profitable success or to expensive failures, and usually involve several distinct areas in the scope of Petroleum Engineering and Science, such as Geology, Petreoleum Engineering, Offshore Engineering and Economics. Therefore, these subjects must be well understood by teams supporting the decision-making process. This work proposes a methodology to support managers in one stage of field development: the definition of the field production system. In order to determinate the production system to be installed in an oil field, attributes such as investment, profitability, safety, environmental preservation and technological experience must be considered. A decision-making team or agent must weight these attributes in order to achieve solutions accordingly to the company strategies and objectives. Combining a few mathematical tools to represent the process, the methodology proposed herein is an approach that considers not only the financial variables involved in a field decision process, but might include other aspects, or attributes, also important to guide a decision. To this end, the application of Multi-Attribute Analysis concepts is suggested. Also, to support the decision-making agent, the approach follows Utility Functions concepts in order to numerically represent the agent trend or inclination to each option. Considering that subjectivity and imprecision are naturally involved in the decision-making process, the approach incorporates Fuzzy Sets Theory concepts as a means of formalizing the computation of this uncertainty.

IMPROVING FIELD PRODUCTION AND THE VALUE OF ASSETS THROUGH ENABLING REAL-TIME WORKFLOWS

2004 ◽  
Vol 44 (1) ◽  
pp. 575 ◽  
Author(s):  
B. Theuveny ◽  
J. Amedick ◽  
A. Kosmala ◽  
J.G. Flores ◽  
H. Soliman
Keyword(s):  
Real Time ◽  
Production Management ◽  
Management Practices ◽  
Oil And Gas ◽  
Selection Process ◽  
Time Frame ◽  
Oil Field ◽  
Legal Constraints ◽  
Diagnostic Applications ◽  
The Impact

Reservoir and production management practices can benefit from the use of information obtained in real-time. This paper focusses specifically on the gains obtained from the continuous monitoring of naturally flowing and artificially lifted wells.The deployment of real-time production workflows is an important enabler to improve the value of oil and gas assets. The impact is seen in areas such as:the improvement of production (well productivity), through the reduction of deferred production and increased productivity;reduction of operating costs (OPEX);reduction of repair time;reduction of capital expenses (CAPEX);capture of best (and worst) practices;increased operational flexibility; andimproved efficiency of workforce.Field examples over a range of applications covering both artificially lifted wells to naturally flowing wells demonstrate the value of real-time monitoring and relevant-time surveillance and diagnostic applications. Examples of permanent monitoring systems installed at subsurface and/or at surface illustrate how operators can optimise the value of new and existing assets. Although much of the technology has been available for years, deployment in actual field operation is still a challenge. Several best practices are suggested to improve implementation success. The human component in this oil field revolution is important and cannot be under-estimated. The success of real-time enabled workflows can only occur if the workforce fully cooperates and buys-in to the solution. The inertia of legacy practices can derail the change management process if not considered early in the implementation.This paper discusses several industry approaches to product and service delivery of real-time enabled production workflows, and the various possible implementations. The commercial and physical implementations of these production workflows can range from remotely hosted solutions with no footprint on the operator premises, to fully integrated solution using and integrating legacy system of the oil and gas company. A segmentation of these approaches facilitates the selection process depending on parameters such as the size of the asset, legal constraints, availability of expertise. The value of the benefits of each of these approaches also provides a better understanding of the probable gains that may be achieved in the short to long-term time frame.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Ghana's petroleum industry: expectations, frustrations and anger in coastal communities

2020 ◽  
Vol 58 (3) ◽  
pp. 397-424
Author(s):  
Jesse Salah Ovadia ◽  
Jasper Abembia Ayelazuno ◽  
James Van Alstine
Keyword(s):  
Oil And Gas ◽  
Local Development ◽  
Petroleum Industry ◽  
Field Research ◽  
Gas Production ◽  
Oil Field ◽  
Oil And Gas Production ◽  
High Expectations ◽  
Petroleum Resources ◽  
Negative Impacts

ABSTRACTWith much fanfare, Ghana's Jubilee Oil Field was discovered in 2007 and began producing oil in 2010. In the six coastal districts nearest the offshore fields, expectations of oil-backed development have been raised. However, there is growing concern over what locals perceive to be negative impacts of oil and gas production. Based on field research conducted in 2010 and 2015 in the same communities in each district, this paper presents a longitudinal study of the impacts (real and perceived) of oil and gas production in Ghana. With few identifiable benefits beyond corporate social responsibility projects often disconnected from local development priorities, communities are growing angrier at their loss of livelihoods, increased social ills and dispossession from land and ocean. Assuming that others must be benefiting from the petroleum resources being extracted near their communities, there is growing frustration. High expectations, real and perceived grievances, and increasing social fragmentation threaten to lead to conflict and underdevelopment.

Study and Comparison of Vibration-Based Intrusive and Non-Intrusive Sand Monitoring System

2013 ◽  
Vol 701 ◽  
pp. 440-444
Author(s):  
Gang Liu ◽  
Peng Tao Liu ◽  
Bao Sheng He
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Oil Production ◽  
Oil Wells ◽  
Oil Field ◽  
Sand Production ◽  
The Status

Sand production is a serious problem during the exploitation of oil wells, and people put forward the concept of limited sand to alleviate this problem. Oil production with limited sanding is an efficient mod of production. In order to complete limited sand exploitation, improve the productivity of oil wells, a real-time sand monitoring system is needed to monitor the status of wells production. Besides acoustic sand monitoring and erosion-based sand monitoring, a vibration-based sand monitoring system with two installing styles is proposed recently. The paper points out the relationships between sand monitoring signals collected under intrusive and non-intrusive installing styles and sanding parameters, which lays a good foundation for further study and actual sand monitoring in oil field.

Computer Implementation of the Dykstra-Parsons Method of Waterflood Calculation

2021 ◽  
Author(s):  
Prosper Kiisi Lekia
Keyword(s):  
Petroleum Industry ◽  
Injection Pressure ◽  
Injection Rate ◽  
Process Time ◽  
Computer Implementation ◽  
Natural Energy ◽  
Recovery Techniques ◽  
Secondary Recovery ◽  
Time Required ◽  
User Friendly

Abstract One of the challenges of the petroleum industry is achieving maximum recovery from oil reservoirs. The natural energy of the reservoir, primary recoveries in most cases do not exceed 20%. To improve recovery, secondary recovery techniques are employed. With secondary recovery techniques such as waterflooding, an incremental recovery ranging from 15 to 25% can be achieved. Several theories and methods have been developed for predicting waterflood performance. The Dykstra-Parson technique stands as the most widely used of these methods. The authors developed a discrete, analytical solution from which the vertical coverage, water-oil ratio, cumulative oil produced, cumulative water produced and injected, and the time required for injection was determined. Reznik et al extended the work of Dykstra and Parson to include exact, analytical, continuous solutions, with explicit solutions for time, constant injection pressure, and constant overall injection rate conditions, property time, real or process time, with the assumption of piston-like displacement. This work presents a computer implementation to compare the results of the Dykstra and Parson method, and the Reznik et al extension. A user-friendly graphical user interface executable application has been developed for both methods using Python 3. The application provides an interactive GUI output for graphs and tables with the python matplotlib module, and Pandastable. The GUI was built with Tkinter and converted to an executable desktop application using Pyinstaller and the Nullsoft Scriptable Install System, to serve as a hands-on tool for petroleum engineers and the industry. The results of the program for both methods gave a close match with that obtained from the simulation performed with Flow (Open Porous Media). The results provided more insight into the underlying principles and applications of the methods.

A Back Allocation Methodology to Estimate the Real-Time Flow and Assist Production Monitoring

2021 ◽  
Author(s):  
Gabriela Chaves ◽  
Danielle Monteiro ◽  
Virgilio José Martins Ferreira
Keyword(s):  
Real Time ◽  
Input Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Time Flow ◽  
Well Model ◽  
Operational Information ◽  
The Individual

Abstract Commingle production nodes are standard practice in the industry to combine multiple segments into one. This practice is adopted at the subsurface or surface to reduce costs, elements (e.g. pipes), and space. However, it leads to one problem: determine the rates of the single elements. This problem is recurrently solved in the platform scenario using the back allocation approach, where the total platform flowrate is used to obtain the individual wells’ flowrates. The wells’ flowrates are crucial to monitor, manage and make operational decisions in order to optimize field production. This work combined outflow (well and flowline) simulation, reservoir inflow, algorithms, and an optimization problem to calculate the wells’ flowrates and give a status about the current well state. Wells stated as unsuited indicates either the input data, the well model, or the well is behaving not as expected. The well status is valuable operational information that can be interpreted, for instance, to indicate the need for a new well testing, or as reliability rate for simulations run. The well flowrates are calculated considering three scenarios the probable, minimum and maximum. Real-time data is used as input data and production well test is used to tune and update well model and parameters routinely. The methodology was applied using a representative offshore oil field with 14 producing wells for two-years production time. The back allocation methodology showed robustness in all cases, labeling the wells properly, calculating the flowrates, and honoring the platform flowrate.

Digital Workflow to Enhance Reservoir Management Strategies for A Complex Oil Field Through Real Time and Advanced Engineering Monitoring Solution

2021 ◽  
Author(s):  
Julieta Alvarez ◽  
Oswaldo Espinola ◽  
Luis Rodrigo Diaz ◽  
Lilith Cruces
Keyword(s):  
Real Time ◽  
Reservoir Characterization ◽  
Management Strategies ◽  
Reservoir Management ◽  
Gas Production ◽  
Oil Field ◽  
Oil Reservoirs ◽  
Production Operation ◽  
Digital Workflow ◽  
Operational Decisions

Abstract Increase recovery from mature oil reservoirs requires the definition of enhanced reservoir management strategies, involving the implementation of advanced methodologies and technologies in the field's operation. This paper presents a digital workflow enabling the integration of commonly isolated elements such as: gauges, flowmeters, inflow control devices; analysis methods and data, used to improve scientific understanding of subsurface flow dynamics and determine improved operational decisions that support field's reservoir management strategy. It also supports evaluation of reservoir extent, hydraulic communication, artificial lift impact in the near-wellbore zone and reservoir response to injected fluids and coning phenomenon. This latest is used as an example to demonstrate the applicability of this workflow to improve and support operational decisions, minimizing water and gas production due to coning, that usually results in increasing production operation costs and it has a direct impact decreasing reservoir energy in mature saturated oil reservoirs. This innovative workflow consists on the continuous interpretation of data from downhole gauges, referred in this paper as data-driven; as well as analytical and numerical simulation methodologies using real-time raw data as an input, referred in this paper as model-driven, not commonly used to analyze near wellbore subsurface phenomena like coning and its impact in surface operation. The resulting analyses are displayed through an extensive visualization tool that provides instant insight to reservoir characterization and productivity groups, improving well and reservoir performance prediction capabilities for complex reservoirs such as mature saturated reservoirs with an associated aquifer, where undesired water and gas production is a continuous challenge that incorporates unexpected operational expenses.

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