The Evolution of Green Completion in BP Khazzan Field

2021 ◽  
Author(s):  
Sultan Al Harrasi ◽  
Naren Jayawickramarajah ◽  
Taimur Al Shidhani ◽  
Daniel White ◽  
Mohamed Najwani
Keyword(s):  
Carbon Emissions ◽  
Environmentally Friendly ◽  
Pilot Project ◽  
Well Testing ◽  
Well Test ◽  
Safety Standards ◽  
Central Processing ◽  
The Usa ◽  
Reduce Emissions ◽  
Reduction Of Co2

Abstract Well Testing is the single largest contributor of carbon emissions during well operations and the industry's aspiration to reduce carbon emissions inspired the bp Oman team to identify innovative ways to reduce emissions from activities in the Khazzan field. Khazzan is characterized by tight reservoirs which requires hydraulic fracturing to release gas from the rock. After fracturing, the wells are tested/cleaned-up by flowing the well fluids and flaring the produced gas and condensate to the atmosphere. The testing removes contaminants – proppant, frac fluid, hydrogen sulphide – that could damage the downstream Central Processing Facility (CPF). ‘Green Completion’ was one of the opportunities that was identified by the bp's Oman team to remove these contaminants in an environmentally friendly manner. A Green Completion is a zero flaring concept – hydrocarbons produced during well test operations are ‘cleaned’ and then routed to processing facilities for export rather than being flared. This concept has been successfully utilized in bp's onshore US operations for over a decade. The team leveraged the experience from the USA, applying this technology to suit the conditions in Oman, but it was not simple nor straight forward. In the last two years, this process has been modified and reinvented for the operations in Oman as the company seeks to strategically reduce its global carbon footprint. In first half of 2018, the bp Wells team initiated a pilot project with the objective of developing Green Completion capability in the Khazzan field. This was the start of the journey to demonstrate bp's commitment to reducing greenhouse gas (CHG) emissions in a sustainable manner. Furthermore, bp's collaborative cross-functional aptitude allowed for expanding the use of Green Completions into the Ghazeer development, which enabled zero-emission well testing of newly drilled wells even before commissioning of the new pipeline infrastructure. Through this initiative, the region has reduced emissions and generated cash by selling the recovered hydrocarbons instead of flaring into the atmosphere during well testing operations. Since Q1 2019, the total reduction of CO2 emissions exceeded 240,000 tonnes of CO2 equivalent, which equates to taking circa 52,000 vehicles off the road for one year. The implementation of this environmentally friendly operation also adhered to strict safety standards. The rigid bp safety process guidelines ensured that all challenges and optimization opportunities were fulfilled in a safe manner. The purpose of this paper is to detail how the team pushed the technical envelope to introduce this technology and share the journey entailing extensive cross-disciplinary cooperation amongst operations, subsurface and wells teams to fulfill the zero emissions objective.

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.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

scholarly journals Data Processing Method of Well Testing

2021 ◽  
Vol 134 (3) ◽  
pp. 35-38
Author(s):  
A. M. Svalov ◽  
Keyword(s):  
Boundary Condition ◽  
Asymptotic Expansion ◽  
Test Data ◽  
Traditional Method ◽  
Processing Method ◽  
Well Testing ◽  
Well Test ◽  
Conductivity Equation ◽  
Complete Asymptotic Expansion ◽  
Data Processing Method

Horner’s traditional method of processing well test data can be improved by a special transformation of the pressure curves, which reduces the time the converted curves reach the asymptotic regimes necessary for processing these data. In this case, to take into account the action of the «skin factor» and the effect of the wellbore, it is necessary to use a more complete asymptotic expansion of the exact solution of the conductivity equation at large values of time. At the same time, this method does not allow to completely eliminate the influence of the wellbore, since the used asymptotic expansion of the solution for small values of time is limited by the existence of a singular point, in the vicinity of which the asymptotic expansion ceases to be valid. To solve this problem, a new method of processing well test data is proposed, which allows completely eliminating the influence of the wellbore. The method is based on the introduction of a modified inflow function to the well, which includes a component of the boundary condition corresponding to the influence of the wellbore.

scholarly journals Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

2021 ◽  
Vol 245 ◽  
pp. 01020
Author(s):  
Aixia Xu ◽  
Xiaoyong Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Utilization ◽  
Total Carbon ◽  
Utilization Rate ◽  
Low Carbon ◽  
Logistics Industry ◽  
Direct Energy ◽  
Reduce Emissions

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

Pressure Analysis for Fractured Wells

1972 ◽  
Author(s):  
Alain C. Gringarten ◽  
Henry J. Ramey ◽  
R. Raghavan
Keyword(s):  
Skin Effect ◽  
Time Pressure ◽  
Physical Nature ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Well Test Analysis ◽  
Test Analysis ◽  
Straight Line ◽  
Short Time

INTRODUCTION During the last few years, there has been an explosion of information in the field of well test analysis. Because of increased physical understanding of transient fluid flow, the entire pressure history of a well test can be analyzed, not just long-time data as in conventional analysis.! It is now often possible to specify the time of beginning of the correct semilog straight line and determine whether the correct straight line has been properly identified. It is also possible to identify wellbore storage effects and the nature of wellbore stimulation as to permeability improvement, or fracturing, and perform quantitative analyses of these effects. These benefits were brought about in the main by attempts to understand the short-time pressure data from well testing, data which were often classified as too complex for analysis. One recent study of short-time pressure behavior2 showed that it was important to specify the physical nature of the stimulation in consideration of stimulated well behavior. That is, statement of the van Everdingen-Hurst infinitesimal skin effect as negative was not sufficient to define short-time well behavior. For instance, acidized {but not acid fraced) and hydraulically fractured wells did not necessarily have the same behavior at early times, even though they might possess the same value of negative skin effect.

scholarly journals Tracking the Spatial–Temporal Evolution of Carbon Emissions in China from 1999 to 2015: A Land Use Perspective

2019 ◽  
Vol 11 (17) ◽  
pp. 4531 ◽  
Author(s):  
Li Wang ◽  
Jie Pei ◽  
Jing Geng ◽  
Zheng Niu
Keyword(s):  
Economic Growth ◽  
Land Use ◽  
Carbon Emissions ◽  
Temporal Evolution ◽  
National Level ◽  
Kuznets Curve ◽  
Annual Growth Rate ◽  
Intergovernmental Panel ◽  
Reduce Emissions

China has been a leader in global carbon emissions since 2006. The question of how to reduce emissions while maintaining stable economic growth is a serious challenge for the country. To achieve this, it is of great significance to track the spatial and temporal evolution of carbon emissions in China during recent decades, which can provide evidence-based scientific guidance for developing mitigation policies. In this study, we calculated the carbon emissions of land use in 1999–2015 using the carbon emissions factor method proposed by the Intergovernmental Panel on Climate Change (IPCC). The Kuznets curve model was used to explore the influence of economic growth and urbanization on carbon emissions at the national and provincial levels. The results indicated that (1) China’s emissions increased from 927.88 million tons (Mt) in 1999 to 2833.91 Mt in 2015 at an average annual growth rate of 12.94%, while carbon sinks grew slightly, from 187.58 Mt to 207.19 Mt. Both emissions and sinks presented significant regional differences, with the Central and Southwest regions acting as the biggest emissions and sink contributors, respectively. (2) Built-up land was the largest land carrier for carbon emissions in China, contributing over 85% to total emissions each year; and (3) at the national level, the relationships between economic growth, urbanization, and carbon emissions presented as inverted U-shaped Kuznets curves, which were also found in the majority of the 30 studied provinces. While carbon emissions may be reaching a peak in China, given the disproportionate role of built-up land in carbon emissions, efforts should be devoted to limiting urbanization and the production of associated carbon emissions.

CSG well testing—Santos' experience in Queensland

2013 ◽  
Vol 53 (1) ◽  
pp. 227
Author(s):  
Czek Hoong Tan ◽  
Guncel Demircan ◽  
Mathias Satyagraha
Keyword(s):  
Radial Flow ◽  
Test Methods ◽  
Well Testing ◽  
Test Results ◽  
Well Test ◽  
Well Performance ◽  
Validity Of Results ◽  
Transient Behaviour ◽  
Key Factor ◽  
Primary Porosity

Permeability of the cleat system is a key factor controlling the productivity of CSG reservoirs and, therefore, the commerciality of development projects. Well testing is routinely used to provide representative values of coal permeability. The authors’ experience has shown pressure transient behaviour in coal reservoirs to be similar to those in primary porosity systems, with pseudo radial flow frequently observed, and the dual-porosity signature largely absent. Despite the authors’ best efforts in test design, large permeability variation and extremely high skin factors have been seen. The authors have run variations of drill stem tests (DSTs), injection tests, and wireline tests to understand the dependency of results to test methods, and the validity of results obtained. Pertinent examples of each type of test are discussed. Finally, recommendations to reconcile well test results to actual well performance are presented.

scholarly journals Spatial cluster and Heterogeneity Research on Energy-Related Carbon Emissions in Districts and Counties of Guangdong Province

2019 ◽  
Vol 79 ◽  
pp. 03019
Author(s):  
Wenxiu Wang ◽  
Shangjun Ke ◽  
Daiqing Zhao ◽  
Guotian Cai
Keyword(s):  
Spatial Autocorrelation ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Guangdong Province ◽  
Total Carbon ◽  
Low Carbon ◽  
Cluster Area ◽  
Reduce Emissions ◽  
Global Spatial Autocorrelation ◽  
High Cluster

Energy-related carbon emissions in districts and counties of Guangdong province from 2005 to 2016 are researched based on spatial econometrics method in this article, and significance cluster area and heterogeneity area are precise pinpointed. Conclusions are as follows: (1) total carbon emissions and per capita carbon emissions exist significance global spatial autocorrelation in the year 2005-2016, and formed significance high-high cluster area in districts and counties of Guangzhou city, Shenzhen city and Dongguan city. It also formed three significance low-low cluster areas in districts and counties of eastern, western and northern of Guangdong province. Low-high heterogeneity area and high -low heterogeneity area often appears in the scope of high-high cluster area and low-low cluster area. (2)Carbon emission intensity not exist significance global spatial autocorrelation, but exist significance cluster area and heterogeneity area in the ecological development areas of eastern, western and northern of Guangdong province. In the end, the paper puts forward the regional and detailed policy recommendations for efficient carbon emission reduction for each cluster type region: carbon high-high cluster areas are priority reduce emissions area, heighten energy saving technology and optimize industrial structure are two grippers to reduce emissions. Low - low carbon emissions concentrated area in western of Guangdong should primarily develop high and new technology industry. Low low carbon emissions concentrated areas and high - high carbon emissions intensity concentrated area for eastern and northern of Guangdong province should try hard to wins ecological compensation at the same time focus on developing ecological tourism.

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