Artificial Intelligence-Based, Automated Rapid Reservoir Assurance and Reservoir Health Diagnostics in a Complex Offshore Mature Field

2021 ◽  
Author(s):  
Mahmoud Elwan ◽  
Meher Surendra ◽  
Shawket Ghedan ◽  
Rami Kansao ◽  
Mahmoud Koresh ◽  
...  
Keyword(s):  
Management Practices ◽  
Water Injection ◽  
Reservoir Management ◽  
Automated Analysis ◽  
Flow Unit ◽  
Full Potential ◽  
Reservoir Pressure ◽  
Key Recovery ◽  
Bubble Point ◽  
Well Production

Abstract The QQ Field in the Gulf of Suez is a mature, geologically complex with multiple stacked, faulted reservoirs, with commingled production between different reservoirs. This paper illustrates the power of an automated tool to perform systematic, rapid, and detailed assessment of the reservoir performance, identify the key recovery obstacles and prepare remedial plans to enable the reservoir to produce to its full potential. The well and reservoir data were processed to compute a series of metrics and key performance indicators at various levels (well, layer, reservoir, well groups, etc.). The tool has several automated modules to facilitate rapid, metric-driven reservoir assurance and management. These modules include: (i) well production/injection allocation, (ii) wells decline curve analysis including event-detection, (iii) pressure and voidage analysis, and (iv) Contact analysis. Using performance analytics, the study quickly identified ways to improve the health of the reservoir and maximize its value. The QQ Field predominantly produces from two formations: Nubia and Nezzazat. Furthermore, there are multiple sub-layers in each formation. Reliable flow unit allocation is critical to gauge contribution of each layer, identify the undrained areas of the reservoir, and locate future development opportunities. The flow unit allocation module incorporates all available data such as PLT/ILT data, completion history, permeability of each flow unit at well level, relative pressures, and water influx model. Based on the allocated production, the current recovery factors in Nubia and Nezzazat are approximately 60% and 20% respectively. Analysis of RFT data reveals good vertical communication across Nubia. However, in some areas there is clear pressure discontinuity across layers. The reservoir pressure has dropped below the bubble point in both formations. As a result, water injection was initiated. The pressure in all parts of Nubia was restored above bubble point. Aquifer influx is sufficient to support the current withdrawal rates and further water injection is unnecessary. However, Nezzazat has a significantly higher reservoir complexity and therefore, shows a large variation in pressure behavior. It needs water injection to maintain the reservoir pressure above the bubble point in all parts of the reservoir. Based on the flow-unit allocation, the voidage replacement ratio (VRR) was calculated for each area and each layer. Even though the overall VRR in the waterflooded areas is above one, the distribution of the injected water is uneven. Redistributing injected water and ensuring that all the areas and all the layers are adequately supported will help to maximize recovery. The prolonged dip in oil price demands extreme efficiency. Sound reservoir management must not require unreasonable time or manpower. The rapid, automated analysis enables quick identification of the key areas for improvement in reservoir management practices and maximize the value of the asset.

Diagnostic Plots for Production Decline During the Transition of Oil Field Development From Depletion to Water Injection

2021 ◽  
Author(s):  
Vil Syrtlanov ◽  
Yury Golovatskiy ◽  
Ivan Ishimov
Keyword(s):  
Water Injection ◽  
Simulation Models ◽  
Oil Field ◽  
Production Data ◽  
Oil Reservoir ◽  
Field Development ◽  
Well Production ◽  
Diagnostic Plots ◽  
Oil Field Development ◽  
Analytical Approaches

Abstract In this paper the simplified way is proposed for predicting the dynamics of liquid production and estimating the parameters of the oil reservoir using diagnostic curves, which are a generalization of analytical approaches, partially compared with the results of calculations on 3D simulation models and with actual well production data.

A Proxy Flow Modelling Workflow to Estimate Gridded Dynamic Properties and Well Production Rates by Deep Learning Algorithms

2021 ◽  
Author(s):  
Soumi Chaki ◽  
Yevgeniy Zagayevskiy ◽  
Terry Wong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dynamic Properties ◽  
Reservoir Management ◽  
Flow Modeling ◽  
Static Properties ◽  
Production Rates ◽  
Reservoir Properties ◽  
Well Production ◽  
Reservoir Simulator

Abstract This paper proposes a deep learning-based framework for proxy flow modeling to predict gridded dynamic petroleum reservoir properties (like pressure and saturation) and production rates for wells in a single framework. It approximates the solution of a full physics-based numerical reservoir simulator, but runs much more rapidly, allowing users to generate results for a much wider range of scenarios in a given time than could be done with a full physics simulator. The proxy can be used for reservoir management tasks like history matching, uncertainty quantification, and field development optimization. A deep-learning based methodology for accurate proxy-flow modeling is presented which combines U-Net (a variant of convolutional neural network) to predict gridded dynamic properties and deep neural network (DNN) models to forecast well production rates. First, gridded dynamic properties, such as reservoir pressure and phase saturations, are predicted from static properties like reservoir rock porosity and absolute permeability using a U-Net. Then, the static properties and the dynamic properties predicted by the U-Net are input to a DNN to predict production rates at the well perforations. The inclusion of U-net predicted pressure and saturations improves the quality of the well rate predictions. The proposed methodology is presented with the synthetic Brugge reservoir discretized into grid blocks. The U-Net input consists of three properties: dynamic gridded reservoir properties (such as pressure or fluid saturation) at the current state, static gridded porosity, and static gridded permeability. The U-Net has only one output property, the target gridded property (such as pressure or saturation) at the next time step. Training and testing datasets are generated by running 13 full physics flow simulations and dividing them in a 12:1 ratio. Nine U-Net models are calibrated to predict pressures/saturations, one for each of the nine grid layers present in the Brugge model. These outputs are then concatenated to obtain the complete pressure/saturation model for all nine layers. The constructed U-Net models match the distributions of generated pressures/saturations of the numerical reservoir simulator with a correlation coefficient value of approximately 0.99 and above 95% accuracy. The DNN models approximate well production rates accurately from U-Net predicted pressures and saturations along with static properties like transmissibility and horizontal permeability. For each well and each well perforation, the production rate is predicted with the DNN model. The use of the constructed proxy flow model generates reservoir predictions within a few minutes compared to the hours or days typically taken by a full physics flow simulator. The direct connection that is established between the gridded static and dynamic properties of the reservoir and well production rates using U-Net and DNN models has not been presented previously. Using only a small number of runs for its training, the workflow matches the numerical reservoir simulator results with reduced computational effort. This helps reservoir engineers make informed decisions more quickly, resulting in more efficient reservoir management.

scholarly journals Demystifying Collaboration in BIM-Based Projects Under Design-Build Procurement

2018 ◽  
pp. 158-190
Author(s):  
Benjamin R. Jowett ◽  
Malak Al Hattab ◽  
Mohamad Kassem
Keyword(s):  
Management Practices ◽  
Project Delivery ◽  
Full Potential ◽  
Integrated Project Delivery ◽  
Building Information Modelling ◽  
Engineering Construction ◽  
Design Build ◽  
Building Information ◽  
Integrated Project ◽  
Value Realization

Building information modelling (BIM) tools and workflows, new procurements methods, and emerging management practices are being adopted on projects to overcome collaboration barriers and improve project performance within the architecture, engineering, construction, and operation (AECO) sector. Academic literature and industry reports recommend the use of collaborative procurement methods such as design and build (DB) procurement and integrated project delivery (IPD) when adopting BIM workflows. However, to date there are little operationalization and empirical evidence of the value realization potential when using BIM in conjunction to these procurement methods. This chapter draws upon five case studies of BIM-based DB projects to analyze and quantify the potential of value realization using clash detection as a use value. The results reveal potential hurdles inhibiting BIM from reaching its full potential. Accordingly, recommended changes to the current processes are suggested to facilitate BIM in enhancing value on DB projects.

scholarly journals Studying of changes in the interaction of well during the water injection process

2019 ◽  
pp. 81-85
Author(s):  
Damir K. Sagitov
Keyword(s):  
Correlation Coefficient ◽  
Pair Correlation ◽  
Water Injection ◽  
Reservoir Pressure ◽  
Maintenance System ◽  
Displacement Front ◽  
Injection Process ◽  
Production Wells

The study of the causes of changes in the effectiveness of the reservoir pressure maintenance system in terms of the interaction of injection and production wells is an important and insufficiently studied problem, especially in terms of the causes of the attenuation of stable connections between the interacting wells. Based on the results of the calculation of the Spearman pair correlation coefficient, the reasons for the change in the interaction of wells during the flooding process at various stages were estimated. Of particular interest are identified four characteristic interactions, which are determined by the periods of formation of the displacement front.

scholarly journals Production Optimization for Natural Flow and ESP Well A Case Study on Well NS-5 Mishrif Formation-Nasriya Oil Field

2020 ◽  
Vol 10 (2) ◽  
pp. 17-35
Author(s):  
Hamzah Amer Abdulameer ◽  
Dr. Sameera Hamd-Allah
Keyword(s):  
Production Rate ◽  
Production Optimization ◽  
Oil Field ◽  
Reservoir Pressure ◽  
Pump Frequency ◽  
Individual Parameter ◽  
Well Production ◽  
Well Design ◽  
Natural Flow ◽  
Reservoir Conditions

As the reservoir conditions are in continuous changing during its life, well production rateand its performance will change and it needs to re-model according to the current situationsand to keep the production rate as high as possible.Well productivity is affected by changing in reservoir pressure, water cut, tubing size andwellhead pressure. For electrical submersible pump (ESP), it will also affected by numberof stages and operating frequency.In general, the production rate increases when reservoir pressure increases and/or water cutdecreases. Also the flow rate increase when tubing size increases and/or wellhead pressuredecreases. For ESP well, production rate increases when number of stages is increasedand/or pump frequency is increased.In this study, a nodal analysis software was used to design one well with natural flow andother with ESP. Reservoir, fluid and well information are taken from actual data of Mishrifformation-Nasriya oil field/ NS-5 well. Well design steps and data required in the modelwill be displayed and the optimization sensitivity keys will be applied on the model todetermine the effect of each individual parameter or when it combined with another one.

Takula Field: Data Acquisition, Interpretation, and Integration for Improved Simulation and Reservoir Management

2002 ◽  
Vol 5 (02) ◽  
pp. 135-145 ◽  
Author(s):  
G.R. King ◽  
W. David ◽  
T. Tokar ◽  
W. Pape ◽  
S.K. Newton ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Simulation Model ◽  
Gamma Ray ◽  
Reservoir Management ◽  
Oil Field ◽  
Flow Unit ◽  
Flow Units ◽  
Unit Scale ◽  
Geostatistical Models ◽  
The Republic

Summary This paper discusses the integration of dynamic reservoir data at the flow-unit scale into the reservoir management and reservoir simulation efforts of the Takula field. The Takula field is currently the most prolific oil field in the Republic of Angola. Introduction The Takula field is the largest producing oil field in the Republic of Angola in terms of cumulative oil production. It is situated in the Block 0 Concession of the Angolan province of Cabinda. It is located approximately 25 miles offshore in water depths ranging from 170 to 215 ft. The field consists of seven stacked, Cretaceous reservoirs. The principal oil-bearing horizon is the Upper Vermelha reservoir. This paper discusses the data acquisition and integration for this reservoir only. The reservoir was discovered in January 1980 with Well 57- 02X. Primary production from the reservoir began in December 1982. The reservoir was placed on a peripheral waterflood in December 1990. Currently, the Upper Vermelha reservoir accounts for approximately 75% of the production from the field. Sound management of mature waterfloods has been identified as a key to maximizing the ultimate recovery and delivering the highest value from the Block 0 Asset.1 Therefore, the objective of the simulation effort was to develop a tool for strategic and dayto- day reservoir management with the intent of managing and optimizing production on a flow-unit basis. Typical day-to-day management activities include designing workovers, identifying new well locations, optimizing injection well profiles, and optimizing sweep efficiencies. To perform these activities, decisions must be made at the scale of the individual flow units. In general, fine-grid geostatistical models are developed from static data, such as openhole log data and core data. Recent developments in reservoir characterization have allowed for the incorporation of some dynamic data, such as pressure-transient data and 4D seismic data, into the geostatistical models. Unfortunately, pressure-transient data are acquired at a test-interval scale (there are typically 3 to 4 test intervals per well, depending on the ability to isolate different zones mechanically in the wellbore), while seismic data are acquired at the reservoir scale. The reservoir surveillance program in the Takula field routinely acquires data at the flow-unit scale. These data include openhole log and wireline pressure data from newly drilled wells and casedhole log and production log (PLT) data from producing/injecting wells. Because of the time-lapse nature of cased-hole log and PLT data, they represent dynamic reservoir data at the flow-unit scale. To achieve the objectives of the modeling effort and optimize production on a flow-unit basis, these dynamic data must be incorporated into the simulation model at the appropriate scale. When these data are incorporated into a simulation model, it is typically done during the history match. There are, however, instances when these data are incorporated during other phases of the study. The objective of this paper, therefore, is to discuss the methods used to integrate the dynamic reservoir data acquired at the flow-unit scale into the Upper Vermelha reservoir simulation model. Reservoir Geology The geology of the Takula field is described in detail in Ref. 2. The aspects of the reservoir geology that are pertinent to this paper are elaborated in this section. Reservoir Stratigraphy. The Takula field consists of seven stacked reservoirs. The principal oil-bearing horizon is the Upper Vermelha reservoir. This reservoir contains an undersaturated, 33°API crude oil. For reservoir management purposes, 36 marker surfaces have been identified in the reservoir. Flow units were then identified as reservoir units separated by areally pervasive vertical flow barriers (nonreservoir rock). This resulted in the identification of 20 flow units. The thickness of these flow units ranges from 5 to 15 ft. Reservoir Structure. The reservoir structure is a faulted anticline that is interpreted to be the result of regional salt tectonics. Closure to the reservoir is provided by faults on the southwestern and northern flanks of the structure and by an oil/water contact (OWC) on the eastern, western, and southern flanks of the structure. A structure map of the reservoir is presented in Fig. 1. Data Acquisition in the Takula Field Openhole Log Program. Most original development wells were logged with a basic log suite of resistivity/gamma ray and density/ neutron logs. In addition, the vertical wells drilled from each well jacket were logged with a sonic log and, occasionally, velocity surveys. All wells drilled after 1993 were logged with long spacing sonic and spectral gamma ray logs. In many wells drilled after December 1997, carbon/oxygen (C/O) logs have been run in open hole to distinguish between formation and injected water.3 A few recent wells have been logged with nuclear magnetic resonance (NMR) logs. The NMR log data, when integrated with data from other logs, have been of value in distinguishing free water from bound water, formation water from injection water, and reservoir rock from nonreservoir rock.

Six Sigma in Human Resources

2017 ◽  
pp. 242-269
Author(s):  
Sushree Lekha Padhi
Keyword(s):  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Six Sigma ◽  
Management Practices ◽  
Full Potential ◽  
Support Functions ◽  
Business Excellence ◽  
Customer Delight ◽  
Voice Of Customer

HR business partner, Business Excellence are some buzzwords in the industry nowadays. Profitability and efficiency are being driven through various strategic initiatives aligned to the vision of the organization. Customer satisfaction is now being replaced by customer delight. Organizations are taking steps ahead of voice of customer. The consumer insights are thoroughly analyzed and interpreted. Data analytics is not restricted to only finance and operation functions but are widely used across the support functions along with line functions. Human resource is now considered as an asset. Organizations are also trying to find out ways to capitalize the full potential of human asset. Various tools and methodologies are paving its way to bring efficient human resource management practices. Six Sigma is one of the tools, which is booming into the application space of Human Resource Management. Six Sigma is being considered as a business process and is helping the in shaping and improving their bottom line by designing and monitoring various activities to reduce the defects.

Penaeid Shrimp Aquaculture

2020 ◽  
pp. 233-258
Author(s):  
Claude E. Boyd ◽  
Lauren N. Jescovitch
Keyword(s):  
Natural Waters ◽  
Management Practices ◽  
Penaeus Monodon ◽  
Penaeid Shrimp ◽  
Culture Method ◽  
Shrimp Aquaculture ◽  
Full Potential ◽  
Specific Pathogen ◽  
Future Demands ◽  
Disease Epidemics

Aquaculture supplies about 60% of the current market demand for shrimp. The entire increase for future demands must come from aquaculture since the capture from natural waters is not expected to increase. Shrimp aquaculture is conducted in many tropical and subtropical countries, but six countries—China, Indonesia, Vietnam, India, Ecuador, and Thailand—produce about 85% of cultured shrimp. Shrimp aquaculture relies on penaeid shrimp species, and two species, Litopenaeus vannamei and Penaeus monodon, account for most of the production. Shrimp aquaculture had an annual value of USD23.6 billion in 2014, making it a major item in international trade. Shrimp are produced almost exclusively in coastal ponds filled with estuarine or seawater. Small shrimp for stocking in ponds are produced in hatcheries mostly from farm-reared broodstock. Production intensity in ponds ranged from 200 to 500 kg/ha/crop in fertilized ponds to 5,000–10,000 kg/ha/crop in ponds with feeding and mechanical aeration. Up to three crops per year may be produced depending upon the location, species, and culture method. Shrimp culture can be seriously affected by viral diseases, and new diseases have been a constant threat to production success. The future of shrimp aquaculture is bright, but for it to reach its full potential, improved broodstock, high health, specific pathogen-free shrimp for stocking, better biosecurity for prevention of disease epidemics, better pond management practices, and more attention to avoiding negative environmental impacts will be necessary.

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