Identification of Potential Candidate's Wells for Workover

2021 ◽  
Author(s):  
Edet Ita Okon ◽  
Dulu Appah
Keyword(s):  
Data Analytics ◽  
Model Development ◽  
Data Gathering ◽  
Principal Component ◽  
Oil Production ◽  
High Water ◽  
Gas Production ◽  
Easy Access ◽  
Potential Candidate ◽  
Potential Wells

Abstract To maximize production from mature fields, it is essential to identify candidate's wells that are not producing up to their potential. Performing periodic interventions or workovers in wells is an established approach for arresting production decline and maximizing production from the fields. However, for mature fields with large well counts, the process of determining the best candidates for well interventions can be complicated and tedious. This can result in less-than-optimal outcomes. Advanced data analytics modeling offers quick and easy access to important information. The main objective of this study is to identify potential candidate wells for workover operation ahead of time so that we can fix them before they become problem. This was achieved via principal component analysis with the aid of XLSTAT in Excel. In this study, we developed a model based on PCA to quickly identify and rank the workover candidate's wells. The dataset used in this project comprises of 66 oil wells and were obtained from a field operating in the Niger Delta. The first step involved data gathering and validation and uploading into XLSTAT software. Data preprocessing procedures were conducted to condition the dataset so as to give optimum performance during model development. A model was built to identify potential wells for workover operation. The results obtained here showed that the wells are separated to areas designated as (A to E). Wells found in area A indicated that they are potential candidates for workover operation. Wells found in area B showed that they are not under immediate danger, but attention would be needed to monitor and prevent increasing water and gas rates in the future. Wells found in area C indicated that they required immediate attention to prevent further decline in oil production. Likewise, wells found in Area D indicated that they also required immediate attention to prevent further decline in oil production. Finally, Wells found in Area E showed that they have highest oil production, lowest water production and moderate gas production, indicating normal condition with no immediate workover operation required. With advanced data analytics modeling, reservoir engineers or geoscientists will now see a bigger picture either field by field or reservoir by reservoir and quicky identify potential candidate wells for workover operation ahead of time before they become a problem. Hence, the results of the analysis can help us to better target wells that are potential candidates for high water cut, high WOR, High gas rates and low oil rates.

scholarly journals Generalized Extreme Value Statistics, Physical Scaling and Forecasts of Oil Production in the Bakken Shale

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3641 ◽  
Author(s):  
Wardana Saputra ◽  
Wissem Kirati ◽  
Tadeusz Patzek
Keyword(s):  
Oil Production ◽  
Extreme Value ◽  
Gas Production ◽  
Extreme Value Statistics ◽  
Well Performance ◽  
Generalized Extreme Value ◽  
Potential Wells ◽  
Well Production ◽  
Industry Standard ◽  
Decline Curve

We aim to replace the current industry-standard empirical forecasts of oil production from hydrofractured horizontal wells in shales with a statistically and physically robust, accurate and precise method of matching historic well performance and predicting well production for up to two more decades. Our Bakken oil forecasting method extends the previous work on predicting fieldwide gas production in the Barnett shale and merges it with our new scaling of oil production in the Bakken. We first divide the existing 14,678 horizontal oil wells in the Bakken into 12 static samples in which reservoir quality and completion technologies are similar. For each sample, we use a purely data-driven non-parametric approach to arrive at an appropriate generalized extreme value (GEV) distribution of oil production from that sample’s dynamic well cohorts with at least 1 , 2 , 3 , ⋯ years on production. From these well cohorts, we stitch together the P 50 , P 10 , and P 90 statistical well prototypes for each sample. These statistical well prototypes are conditioned by well attrition, hydrofracture deterioration, pressure interference, well interference, progress in technology, and so forth. So far, there has been no physical scaling. Now we fit the parameters of our physical scaling model to the statistical well prototypes, and obtain a smooth extrapolation of oil production that is mechanistic, and not just a decline curve. At late times, we add radial inflow from the outside. By calculating the number of potential wells per square mile of each Bakken region (core and noncore), and scheduling future drilling programs, we stack up the extended well prototypes to obtain the plausible forecasts of oil production in the Bakken. We predict that Bakken will ultimately produce 5 billion barrels of oil from the existing wells, with the possible addition of 2 and 6 billion barrels from core and noncore areas, respectively.

scholarly journals Generalized Extreme Value Statistics, Physical Scaling and Forecasts of Oil Production in the Bakken Shale

2019 ◽  
Author(s):  
Wardana Saputra ◽  
Wissem Kirati ◽  
Tadeusz Patzek
Keyword(s):  
Oil Production ◽  
Gas Production ◽  
Extreme Value Statistics ◽  
Barnett Shale ◽  
Potential Wells ◽  
Production Forecasting ◽  
Industry Standard ◽  
Depositional Settings ◽  
Forecasting Method ◽  
Bakken Shale

We replace the current industry-standard empirical forecasts of oil production from hydrofractured horizontal wells in shales with a statistically and physically robust, accurate and precise approach, using the Bakken shale as an illustration. The proposed oil production forecasting method extends our previous work on predicting fieldwide gas production in the Barnett shale and merges it with our new scaling of oil production in shales. We first divide the existing 14,678 horizontal oil wells in the Bakken into 12 static samples in which depositional settings and completion technologies are similar. For each sample, we construct a purely data-driven P50 well prototype by merging the GEV distribution fits of oil production from appropriate well cohorts. We fit the parameters of our physics-based scaling curve to the statistical well prototypes, and obtain their smooth extrapolations to 30 years on production. By calculating the number of potential wells of each Bakken region, and scheduling future drilling programs, we stack up the extended well prototypes to achieve the most plausible forecast. We predict that Bakken will ultimately produce 5 billion barrels of oil from the existing wells, with the possible increments of 2 and 6 billion barrels from core and noncore areas.

scholarly journals HealthTEC Innovation Design - a proposal for a novel Master degree program based on Unmet Clinical Need, global Healthcare Challenges, and 21st century skills

2020 ◽  
Vol 6 (3) ◽  
pp. 599-603
Author(s):  
Michael Friebe
Keyword(s):  
Machine Learning ◽  
21St Century ◽  
21St Century Skills ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Data Gathering ◽  
Healthcare Systems ◽  
Disruptive Innovation ◽  
Incremental Innovation ◽  
Healthcare Provision

AbstractThe effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.

scholarly journals Quantify Coal Macrolithotypes of a Whole Coal Seam: A Method Combing Multiple Geophysical Logging and Principal Component Analysis

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 213
Author(s):  
Chao Cui ◽  
Suoliang Chang ◽  
Yanbin Yao ◽  
Lutong Cao
Keyword(s):  
Principal Component Analysis ◽  
Coal Seam ◽  
Coalbed Methane ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Production ◽  
Index Model ◽  
Geophysical Logging ◽  
The Relationship ◽  
Observation Results

Coal macrolithotypes control the reservoir heterogeneity, which plays a significant role in the exploration and development of coalbed methane. Traditional methods for coal macrolithotype evaluation often rely on core observation, but these techniques are non-economical and insufficient. The geophysical logging data are easily available for coalbed methane exploration; thus, it is necessary to find a relationship between core observation results and wireline logging data, and then to provide a new method to quantify coal macrolithotypes of a whole coal seam. In this study, we propose a L-Index model by combing the multiple geophysical logging data with principal component analysis, and we use the L-Index model to quantitatively evaluate the vertical and regional distributions of the macrolithotypes of No. 3 coal seam in Zhengzhuang field, southern Qinshui basin. Moreover, we also proposed a S-Index model to quantitatively evaluate the general brightness of a whole coal seam: the increase of the S-Index from 1 to 3.7, indicates decreasing brightness, i.e., from bright coal to dull coal. Finally, we discussed the relationship between S-Index and the hydro-fracturing effect. It was found that the coal seam with low S-Index values can easily form long extending fractures during hydraulic fracturing. Therefore, the lower S-Index values indicate much more favorable gas production potential in the Zhengzhuang field. This study provides a new methodology to evaluate coal macrolithotypes by using geophysical logging data.

New Gel Aggregates To Improve Sweep Efficiency During Waterflooding

2011 ◽  
Vol 14 (01) ◽  
pp. 120-128 ◽  
Author(s):  
Guanglun Lei ◽  
Lingling Li ◽  
Hisham A. Nasr-El-Din
Keyword(s):  
Low Cost ◽  
Oil Production ◽  
High Water ◽  
Resistance Factor ◽  
Water Production ◽  
Sweep Efficiency ◽  
Low Viscosity ◽  
Productivity Decline ◽  
High Water Cut ◽  
Water Cut

Summary A common problem for oil production is excessive water production, which can lead to rapid productivity decline and significant increases in operating costs. The result is often a premature shut-in of wells because production has become uneconomical. In water injectors, the injection profiles are uneven and, as a result, large amounts of oil are left behind the water front. Many chemical systems have been used to control water production and improve recovery from reservoirs with high water cut. Inorganic gels have low viscosity and can be pumped using typical field mixing and injection equipment. Polymer or crosslinked gels, especially polyacrylamide-based systems, are mainly used because of their relatively low cost and their supposed selectivity. In this paper, microspheres (5–30 μm) were synthesized using acrylamide monomers crosslinked with an organic crosslinker. They can be suspended in water and can be pumped in sandstone formations. They can plug some of the pore throats and, thus, force injected water to change its direction and increase the sweep efficiency. A high-pressure/high-temperature (HP/HT) rheometer was used to measure G (elastic modulus) and G" (viscous modulus) of these aggregates. Experimental results indicate that these microspheres are stable in solutions with 20,000 ppm NaCl at 175°F. They can expand up to five times their original size in deionized water and show good elasticity. The results of sandpack tests show that the microspheres can flow through cores with permeability greater than 500 md and can increase the resistance factor by eight to 25 times and the residual resistance factor by nine times. The addition of microspheres to polymer solutions increased the resistance factor beyond that obtained with the polymer solution alone. Field data using microspheres showed significant improvements in the injection profile and enhancements in oil production.

Unlocking By-Passed Oil with Autonomous Inflow Control Devices Through an Integrated Approach

2021 ◽  
Author(s):  
Pawan Agrawal ◽  
Sharifa Yousif ◽  
Ahmed Shokry ◽  
Talha Saqib ◽  
Osama Keshtta ◽  
...  
Keyword(s):  
Continuous Production ◽  
Oil Production ◽  
Gas Production ◽  
Oil Field ◽  
Production Performance ◽  
Fluid Distribution ◽  
Flow Profile ◽  
Horizontal Drain ◽  
Control Devices

Abstract In a giant offshore UAE carbonate oil field, challenges related to advanced maturity, presence of a huge gas-cap and reservoir heterogeneities have impacted production performance. More than 30% of oil producers are closed due to gas front advance and this percentage is increasing with time. The viability of future developments is highly impacted by lower completion design and ways to limit gas breakthrough. Autonomous inflow-control devices (AICD's) are seen as a viable lower completion method to mitigate gas production while allowing oil production, but their effect on pressure drawdown must be carefully accounted for, in a context of particularly high export pressure. A first AICD completion was tested in 2020, after a careful selection amongst high-GOR wells and a diagnosis of underlying gas production mechanisms. The selected pilot is an open-hole horizontal drain closed due to high GOR. Its production profile was investigated through a baseline production log. Several AICD designs were simulated using a nodal analysis model to account for the export pressure. Reservoir simulation was used to evaluate the long-term performance of short-listed scenarios. The integrated process involved all disciplines, from geology, reservoir engineering, petrophysics, to petroleum and completion engineering. In the finally selected design, only the high-permeability heel part of the horizontal drain was covered by AICDs, whereas the rest was completed with pre-perforated liner intervals, separated with swell packers. It was considered that a balance between gas isolation and pressure draw-down reduction had to be found to ensure production viability for such pilot evaluation. Subsequent to the re-completion, the well could be produced at low GOR, and a second production log confirmed the effectiveness of AICDs in isolating free gas production, while enhancing healthy oil production from the deeper part of the drain. Continuous production monitoring, and other flow profile surveys, will complete the evaluation of AICD effectiveness and its adaptability to evolving pressure and fluid distribution within the reservoir. Several lessons will be learnt from this first AICD pilot, particularly related to the criticality of fully integrated subsurface understanding, evaluation, and completion design studies. The use of AICD technology appears promising for retrofit solutions in high-GOR inactive strings, prolonging well life and increasing reserves. Regarding newly drilled wells, dedicated efforts are underway to associate this technology with enhanced reservoir evaluation methods, allowing to directly design the lower completion based on diagnosed reservoir heterogeneities. Reduced export pressure and artificial lift will feature in future field development phases, and offer the flexibility to extend the use of AICD's. The current technology evaluation phases are however crucial in the definition of such technology deployments and the confirmation of their long-term viability.

Developing the Organisational Justice Scale and Examining Teachers' and School Administrators' Views about Organisational Justice in Primary Schools

2021 ◽  
pp. 33-50
Author(s):  
Hasan Basri Memduhoðlu ◽  
Ali Ýhsan Yildiz
Keyword(s):  
Factor Analysis ◽  
Primary Schools ◽  
School Administrators ◽  
Data Gathering ◽  
Principal Component ◽  
Measurement Tool ◽  
Principal Component Factor Analysis ◽  
Reliable Measurement ◽  
Organisational Justice ◽  
Confirmatory Factor

The purpose of this study is to develop a reliable and valid measurement tool to explore views about organisational justice in schools and to examine teachers' and school administrators' views about organisational justice in primary schools. The sample of the study consisted of a total of 455 participants, 176 school administrators and 279 teachers from the primary schools in the Centre of Van. The Organisational Justice Scale, developed by the authors, was employed as data gathering tool. Principal Component Factor Analysis was used to determine the content and construct validities of the scale and Confirmatory Factor Analysis was employed to evaluate the obtained results. As a result of the study, the developed Organisational Justice Scale (OJS) was found to be a valid and reliable measurement tool for school applications.

Physicochemical, Interaction & Topological Descriptors vs. hMAO-A Inhibition of Aplysinopsin Analogs: A Boulevard to the Discovery of Semi-Synthetic Anti-Depression Agents

2021 ◽  
Vol 22 ◽  
Author(s):  
Rajeev K. Singla ◽  
Ghulam Md Ashraf ◽  
Magdah Ganash ◽  
Varadaraj Bhat G ◽  
Bairong Shen
Keyword(s):  
Model Development ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Topological Descriptors ◽  
Good Prediction ◽  
Physicochemical Interaction ◽  
Human Beings ◽  
2D Qsar

Background: Neurological disorder, depression is the globally 4th leading cause of chronic disabilities in human beings. Objective: This study aimed to model a 2D-QSAR equation that can facilitate the researchers to design better aplysinopsin analogs with potent hMAO-A inhibition. Methods: Aplysinopsin analogs dataset were subjected to ADME assessment for drug-likeness suitability using StarDrop software before modeled equation. 2D-QSAR equations were generated using VLife MDS 4.6. Dataset was segregated into training and test set using different methodologies, followed by variable selection. Model development was done using principal component regression, partial least square regression, and multiple regression. Results: The dataset has successfully qualified the drug-likeness criteria in ADME simulation, with more than 90% of molecules cleared the ideal conditions including intrinsic solubility, hydrophobicity, CYP3A4 2C9pKi, hERG pIC50, etc. 112 models were developed using multiparametric consideration of methodologies. The best six models were discussed with their extent of significance and prediction capabilities. ALP97 was emerged out as the most significant model out of all, with ~83% of the variance in the training set, the internal predictive ability of ~74% while having the external predictive capability of ~79%. Conclusion: ADME assessment suggested that aplysinopsin analogs are worth investigating. Interaction among the descriptors in a way of summation or multiplication products, are quite influential and yielding significant 2D-QSAR models with good prediction efficiency. This model can be used for the design of a more potent hMAO-A inhibitor having an aplysinopsin scaffold, which can then contribute to the treatment of depression and other neurological disorders.

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.

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