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Collaborating Authors
Huang, Qingfeng
A Comprehensive Analysis of Data Driven Techniques to Quantify Injector Producer Relationships
Ore, Tobi (Resermine) | Davudov, Davud (Resermine) | Malkov, Anton (Resermine) | Venkatraman, Ashwin (Resermine) | Al-Aulaqi, Talal (Resermine) | Singh, Gurpreet (Resermine) | Dindoruk, Birol (University of Houston) | Djanuar, Yanfidra (Dragon Oil) | Huang, Qingfeng (Dragon Oil) | Adnan, Izwan (Dragon Oil) | Lozano, Jose (Dragon Oil) | Ameish, Giamal (Dragon Oil) | Gibrata, Muhammad (Dragon Oil)
Abstract Water flooding is an established method of secondary recovery to increase oil production in conventional reservoirs. Analytical models such as capacitance resistance models (CRM) have been used to understand the connectivity between injectors and producers to drive optimization. However, these methods are not applicable to waterflood fields at the initial stage of life with limited data (less than 2 years of injection history). In this work, a novel approach is presented that combines analytics and machine learning to process data and hence quantify connectivity for optimization strategies. A combination of statistical (cross-correlation, mutual information) and machine learning (linear regression, random forest) methods are used to understand the relationship between measured injection and production data from wells. This workflow is first validated using synthetic simulation data with known reservoir heterogeneities as well as known connectivity between wells. Each of the four methods is validated by comparing the result with the CRM results, and it was found that each method provides specific insights and has its associated limitations making it necessary to combine these results for a successful interpretation of connectivity. The proposed workflow is applied to a complex offshore Caspian Sea field with 49 production wells and 8 injection wells. It was observed that implementing the diffusivity filter in the models while being computationally expensive, offers additional insights into the transmissibility between injector producer pairs. The machine learning approach addresses injection time delay through feature engineering, and applying a diffusive filter determines effective injection rates as a function of dissipation through the reservoir. Hence, the combined interpretation of connectivity from the different methods resulted in a better understanding of the field. The presented approach can be extended to similar waterflood systems helping companies realize the benefits of digitization, in not just accessing data, but also using data through such novel workflows that can help evaluate and continuously optimize injection processes.
Advanced PVT and Core Analysis for Enhanced Oil Recovery Study of Unconsolidated Sandstone Reservoir
Gibrata, Muhammad A (Dragon Oil (ENOC)) | Ameish, Giamal (Dragon Oil (ENOC)) | Djanuar, Yanfidra (Dragon Oil (ENOC)) | Eldali, Magdi (Dragon Oil (ENOC)) | Huang, Qingfeng (Dragon Oil (ENOC)) | Lozano, Jose (Dragon Oil (ENOC)) | Ali, Nizarudeen (Schlumberger) | Mansour, Bashar (Schlumberger)
Abstract In the matured unconsolidated sandstone reservoir of oil field, enhanced oil recovery (EOR) is important to be implemented. It is to ensure the oil production with the optimum recovery from the reservoir. It requires an integration multi disciplines rock and fluid properties evaluation. In this EOR Study with core-gas injection, it has used engineering of gas injection in advanced PVT analysis that requires a series of laboratory data to properly understand the injection solvent/reservoir fluid phase behavior and EOR displacement in core flooding. The PVT study has been performed, initially on the gas miscibility and phase behavior that consists swelling test, minimum miscibility pressure (MMP), vapour liquid equilibrium (VLE) and multi-contact evaluation and flow assurance studies. The oil and water from the particular reservoir conditions have been used in the analysis. Then it continued with reservoir characterization, core-gas injection and water alternating gas (WAG) analysis. It has involved an in-house integrated petrophysical, geological, reservoir characterization and model in the EOR study. A 3D computed tomography (CT) image and the available RRT model have been used for core plugs selection of the EOR study. The unconsolidated core samples have been plugged successfully by liquid nitrogen and cleaned, inject the higher salinity water until reaching a high water cut, inject the low salinity water in the same manner, and the wettability restoration a steady-state relative permeability with the selected fluids. The steady-state floods (gas-oil, water-oil and gas-water) have been designed to obtain relative permeability on plugs representing selected reservoir rock types at reservoir conditions (pressure and temperature). A digital rock analysis with steady state floods on the same rock types is validated, then digital rock analysis is used to obtain relative permeability data on all remaining rock types for fast time and effective cost. The saturation is determined by in situ saturation monitoring (ISSM) with X-Ray attenuation through the core. Injection continues until the pressure drop across the core sample and the measured saturation are stable. Gas and oil are injected into a sample initially at initial water saturation (Swi) with increasing gas/oil ratios up to maximum gas injection and the sample is at residual oil saturation. The optimum conditions for WAG and related parameters controlled by the core properties on the EOR displacement analysis are defined. This special integrated approach of EOR study has provided the reliable technical basis and assurance for the development of unconsolidated sandstone reservoirs. The oil recovery with current reservoir conditions range has been measured. It has benefits to maximize oil recovery from current main producing reservoir with utilizing the available gas, fluid, core, logs, production and others reservoir data which is important for successful of the field development.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.52)
- Asia > Turkmenistan > Caspian Sea > Cheleken Contract Area > Block 2 > Lam Field > Zone 7 Formation (0.99)
- Asia > Turkmenistan > Caspian Sea > Cheleken Contract Area > Block 2 > Lam Field > Zone 6 Formation (0.99)
- Asia > Turkmenistan > Caspian Sea > Cheleken Contract Area > Block 2 > Lam Field > Zone 5 Formation (0.99)
- Asia > Turkmenistan > Caspian Sea > Cheleken Contract Area > Block 2 > Lam Field > Zone 4 Formation (0.99)
Abstract Having a robust field development plan (FDP) for mid-size mature oil fields generally poses considerable challenges in the context of the integrational elements of production forecast, operational environment, projects and surface facilities. An integrated FDP combined with data analytics and artificial intelligence (AI) has been introduced and deployed in a heavily compartmentalized offshore field of Turkmenistan. An integrated approach through data-centric analytics and AI has been proposed for an optimal FDP. It consists of four aspects: model integration, time-series forecast (TSF) of production, AI-assisted operation-schedule generation, and evaluation and selection of scenarios. Firstly, model integration is performed as bringing together both multi-discipline raw data from field measurement and their interpretations that change non-linearly. Secondly, model integration aids in the application of AI for production forecast. A unique AI technique was built to allow raw data and interpretation. Illustratively, the model is capable of forecasting decline curves matching the history production. Meanwhile, engineersโ production forecast inheriting from simulation, machine learning or type curves is also constructed by understanding how/why human-driven forecasts differ from the measured decline and incorporating those insights. In addition, AI-assisted scheduler efficiently allocates resources for operational activities, considering the well planning nature, intrinsic operation properties, project planning process, surface facilities and expenditures. Resources are thus utilized for optimal schedules. Finally, evaluation and selection of FDP scenarios take place by considering the multidimensional matrix of factors. Multiple scenarios are generated and scored, reacting to the change of factors. AI-powered optimization is availed to recommend the most efficient tradeoffs between production and carbon generation. The implementation of the integrated FDP approach has been successfully applied for the generation of production profiles and operation schedules, which reduces the time by 80% and increasing accuracy by 55%. Production forecast for existing wells and future wells proved to be reliable. It achieved the production targets with proper allocation of schedules, by considering multi-discipline constraints. Through AI-assisted scheduler, different types of rigs were properly assigned to the planned wells, which requires additional rigs based on the outcome. The model was agile to the change and sensitivities of wells requirement, projects uncertainties and cost changes. The optimum FDP scenario was recommended for the business decision, operation guide and execution. This approach represents a novel and innovative means of integrating and optimizing FDP considering complex factors using AI methods. It is efficient in merging raw data and interpretations for model integration. It accommodates changes and uncertainties from multiple aspects and efficiently generates optimum FDP in a few days rather than months for giant fields. It is the first robust tool that unites subsurface properties, reservoir engineering, production, drilling, projects, engineering and finance for the corporate FDP.
A Novel Approach to Combine Models to Evaluate Interwell Connectivity in a Waterflooded Reservoir with Limited Injection History
Djanuar, Yanfidra (Dragon Oil) | Huang, Qingfeng (Dragon Oil) | Adnan, Izwan (Dragon Oil) | Lozano, Jose (Dragon Oil) | Ameish, Giamal (Dragon Oil) | Gibrata, Muhammad (Dragon Oil) | Venkatraman, Ashwin (Resermine) | Malkov, Anton (Resermine) | Davudov, Davud (Resermine) | A Rahman, Rosmawati (Resermine) | Al-Aulaqi, Talal (Resermine) | Dindoruk, Birol (University of Houston)
Abstract Waterflooding is one of the most widely implemented enhanced recovery in mature oil fields. In the absence of a reliable reservoir model, waterflood optimization can be a challenge. The availability of continuous recording of production, injection and well data can be utilized to improve reservoir management in this novel approach. This study presents a new approach using Machine Learning (ML) technique through multiple signal analysis to optimize waterflood operation in a brownfield offshore Caspian Sea. To evaluate injection efficiency on oil production, firstly the interwell connectivity between injectors and producers are determined. However, because of the complexities associated with the reservoir and the data, it has been achieved through analyzing various available signal types which are informative and responsive to injection rates. Results obtained from multiple signals are then aggregated to identify the injector-producer pair connectivity. Next, production well performances are evaluated through multiple diagnostic models. Finally, the impact of injectors on oil production rates are analyzed and injector efficiencies are determined to establish a more efficient waterflooding strategy. The proposed methodology has been applied to a reservoir with around 50 producers and 7 injectors. The interwell connectivity between pairs have been identified and ranked. Using data analytics techniques on multiple surveillance data sets, the analysis of the waterflood is achieved more swiftly and accurately. It was observed that for this specific case, the most informative signals that help determine connectivity are the water cut, and water production rate. The identified injector-producer connections obtained from these models were further verified and compared well with additional available surveillance data on tracers for this reservoir. Understanding these leads to devising optimum waterflooding strategies such as diverting more injection water to the more efficient injectors and less injection water to the inefficient injectors. A novel multi-signal analysis using ML techniques is proposed that combines multiple data being collected as part of surveillance. The presented approach can be extended to similar waterfloods to help with optimizing the waterflooding strategy. This new approach helps with current digitization strategies in oil companies that seek to obtain faster and consistent solutions to accelerate decision making and as an alternative to cases especially where reservoir model is poorly defined.
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.36)
The Intelligent Field Development Plan Through Integrated Cloud Computing and Artificial Intelligence AI Solutions
Ramatullayev, Samat (Schlumberger) | Su, Shi (Schlumberger) | Rat, Coriolan (Schlumberger) | Maarouf, Alaa (Schlumberger) | Mihai, Monica (Schlumberger) | Mustapha, Hussein (Schlumberger) | Djanuar, Yanfidra (Dragon Oil, Holdings Ltd.) | Huang, Qingfeng (Dragon Oil, Holdings Ltd.) | Rouis, Lamia (Dragon Oil, Holdings Ltd.)
Abstract Brownfield field development plans (FDP) must be revisited on a regular basis to ensure the generation of production enhancement opportunities and to unlock challenging untapped reserves. However, for decades, the conventional workflows have remained largely unchanged, inefficient, and time-consuming. The aim of this paper is to demonstrate that combination of the cutting-edge cloud computing technology along with artificial intelligence (AI) and machine learning (ML) solutions enable an optimization plan to be delivered in weeks rather than months with higher confidence. During this FDP optimization process, every stage necessitates the use of smart components (AI & ML techniques) starting from reservoir/production data analytics to history match and forecast. A combined cloud computing and AI solutions are introduced. First, several static and dynamic uncertainty parameters are identified, which are inherited from static modelling and the history match. Second, the elastic cloud computing technology is harnessed to perform hundreds to thousands of history match scenarios with the uncertainty parameters in a much shorter period. Then AI techniques are applied to extract the dominant key features and determine the most likely values. During the FDP optimization process, the data liberation paved the way for intelligent well placement which identifies the "sweet spots" using a probabilistic approach, facilitating the identification and quantification of by-passed oil. The use of AI-assisted analytics revealed how the gas-oil ratio behavior of various wells drilled at various locations in the field changed over time. It also explained why this behavior was observed in one region of the reservoir when another nearby reservoir was not suffering from the same phenomenon. The cloud computing technology allowed to screen hundreds of uncertainty cases using high-resolution reservoir simulator within an hour. The results of the screening runs were fed into an AI optimizer, which produced the best possible combination of uncertainty parameters, resulting in an ensemble of history-matched cases with the lowest mismatch objective functions. We used an intuitive history matching analysis solution that can visualize mismatch quality of all wells of various parameters in an automated manner to determine the history matching quality of an ensemble of cases. Finally, the cloud ecosystem's data liberation capability enabled the implementation of an intelligent algorithm for the identification of new infill wells. The approach serves as a benchmark for optimizing FDP of any reservoir by orders of magnitude faster compared to conventional workflows. The methodology is unique in that it uses cloud computing technology and cutting-edge AI methods to create an integrated intelligent framework for FDP that generates rapid insights and reliable results, accelerates decision making, and speeds up the entire process by orders of magnitude.
- North America > United States > Texas > Terry County (0.24)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.16)
- Geology > Structural Geology (0.94)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.68)
- North America > United States > Texas > Permian Basin > Midland Basin > BrownField Field > Strawn Formation (0.99)
- North America > United States > Texas > Permian Basin > Midland Basin > BrownField Field > Canyon Formation (0.99)
Research on Tubing and Casing Anti-Bending Technology for Salt Cavern Gas Storage Cavity Construction
Cen, Xueqi (Oil Exploration and Production Research Institute, SINOPEC) | Zeng, Hao (Oil Exploration and Production Research Institute, SINOPEC) | Wang, Haibo (Oil Exploration and Production Research Institute, SINOPEC) | Huang, Xiao (Jianghan Oilfield Branch Company, SINOPEC) | Zhang, Rusheng (Oil Exploration and Production Research Institute, SINOPEC) | Wang, Lei (CNPC Research Institute of Safety&Environment Technology) | Gao, Shengen (PetroChina Research Institute of Petroleum Exploration & Development) | Liu, Fang (Beijing Tongzhou District People's Court) | Huang, Qingfeng (Dragon Oil) | Wu, Junwen (Oil Exploration and Production Research Institute, SINOPEC) | Zhang, Le (Oil Exploration and Production Research Institute, SINOPEC)
Abstract Benefiting from its outstanding gas injection-production capability and fully recoverable cushion gas, salt cavern gas storage technology was developed rapidly in recent years. Wangchang salt cave gas storage is characterized by deep burial depth and multiple interlayers. The project of water-soluble cavity construction faces problem of serious tubing and casing bending and deformation. Therefore, development of effective tubing and casing string damage-prevention and risk control method remains an ongoing challenge. Both theoretical study and field tests are presented in this article. Through field observation, mechanical analysis and production data analysis, it was identified that the main reasons causing tubing and casing bending are liquid-solid coupling instability, interlayer rock impact and smashing, as well as pipe string wear and corrosion. By optimizing the tubing and casing strings design and adjusting production parameters, tubing and casing bending problem could be effectively improved. Besides, remedial measures to deal with bending pipe string were proposed. Critical destabilizing flow rate of the tubing and casing was calculated under different diameter and wall thickness conditions. Analysis of calculation results showed that the current water injection volume of the Wangchang gas storage well was higher than the critical displacement. It was verified that the liquid-solid coupling instability was the main reason causing tubing and casing bending in the gas storage well. Field data analysis showed that large water injection displacement and high water injection pressure fluctuation was more likely to cause bending and damage of tubing and casing. Technical measures are listed as following: (1) strengthening the steel grade wall thickness of intermediate casing; (2) equipping the central tubing column with a drop connector to optimize the distance between the central tubing shoe and the middle casing shoe; (3) Optimizing the cavity water circulation method to stabilize the water injection pressure and displacement. The anti-bending technology presented in this study unlocks a critical bottleneck during the salt cave building progress, which is the tubing and casing bending and deformation problem. With the application of this method, efficiency of water-soluble cavity building can be greatly improved. More importantly, this technology might pave a new way for the deep multi-interlayer salt cavern gas storage design.
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Abstract Hysteresis commonly exists in relative permeability curves, especially during a cyclic process. To describe hysteresis impact on fluid behaviors, scanning curves are normally applied in numerical simulation models. Hysteresis pertains to saturation history, in which scanning curves and trapped oil saturation are dependent on the initial oil saturation. For mixed-wet carbonate reservoirs that have thick transition zones, initial water saturations greater than irreducible water saturations are distributed conforming to saturation-height functions and rock types. Therefore, water-flood follows the scanning curve path where intermediate saturation reversal occurs, altering the trapped oil saturation. In the field development plan during water-flooding process, infill drilling identification is traditionally based on simulation model remaining-oil-in-place map or single residual oil saturation attributed to rock types. In this paper, hysteresis-model derived recoverable oil is computed from history-matched model and employed to assist wells planning. An automated infill-drilling program has been coded by considering Killough hysteresis model. First, initial and current saturation at development time are extracted, and end-point residual saturation is identified from saturation functions. Second, the amount of trapped and up-to-date remaining recoverable oil is calculated for each grid cell. Then, the optimum infill locations are identified to maximize contact area with mobile oil. Completion data are generated as output for reservoir simulation. Since optimum infill locations are identified, better oil-recovery is achieved by implementing the proposed approach, which targets both oil zone and transition zone. Single residual oil by rock types may not help identify the anticipated wells locations in the mature reservoirs. The approach is validated by suggesting less and optimum number of wells while bringing higher recovery according to simulation results. Potential of Enhanced Oil Recovery (EOR) can be further evaluated after quantifying the remaining oil saturation. In the transition zone, larger pores are filled with oil and the trapped oil saturation is lower than that above dry oil intervals, enabling possible wells production in the transition zone. Initial-saturation-dependent residual oil saturation is used to better map the infill drilling. Rather than adopting the single residual oil saturation by roc type, this approach considers hysteresis-led trapped oil saturation for the preliminary automatic wells screening and optimization prior to dynamic simulation. This process is repeatable and adjustable in line with the development strategy. For the giant and mature oil reservoirs, it demonstrates a better and more efficient development plan.
- North America > United States (1.00)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.16)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Reduction of residual oil saturation (1.00)
- (2 more...)
A Novel Multi-Density Dynamic Well Killing Method for Ultra-Deep Wells and the Simulation System
Zhou, Haobo (SINOPEC Research Institute of Petroleum Engineering) | Sun, Mingguang (SINOPEC Research Institute of Petroleum Engineering) | Niu, Xinming (SINOPEC Research Institute of Petroleum Engineering) | Zhang, Jinshuang (SINOPEC Research Institute of Petroleum Engineering) | Huang, Qingfeng (ADNOC Offshore)
Abstract Tremendous amounts of oil and gas reservoir are located in ultra-deep formations, such as Shunbei and Shuntuo block in Tarim basin of China, some reservoirs buried deeper than 8000m and which are major exploration areas of SINOPEC. The reservoirs in these areas are full of fractures and cavities which resulting in a series of drilling problems, especially well control safety problems. Therefore, the novel well killing method for ultra-deep wells is very important. Firstly, in order to reduce the well head pressure (WHP) and balance the formation pressure of ultra-deep well quickly in the killing well process, a novel multi-density dynamic well killing method was established, through which we can use two different killing fluids to kill well and one of them is high density fluid. A comprehensive multi-density killing fluids design method was proposed based on the theoretical analysis, through which the optimal density and volume of different fluids can be obtained easily. And based on the optimal killing fluid parameters, we can not only balance formation pressure quickly but also avoid fracturing formation. Secondly, according to the density contrast of operating fluids, a vacuum (U-Tube effect) will appear at the inlet in the well killing process without dynamic controlling of killing parameters, an integrated U-Tube effect analytical model was established based on the Euler equation. For preventing result into a vacuum phenomenon, a real-time calculation model for killing parameters dynamic adjusting was proposed, in which the multiphase flow model was take into account. Based on this model the real-time stand pipe pressure (SPP), WHP and pump flow rate in different stages of well killing can be obtained easily. Moreover, a series of system software for parameters calculating and simulating of multi-density dynamic killing well was developed, through which we can accurate calculating dynamic parameters and hydraulics. The proposed model has been applied in ultra-deep well kick control in SINOPEC Northwest oil filed, the simulating result show that the proposed method and software system can give precisely killing parameters design and simulation. Based on the proposed killing well method, we can quickly reduce operating risk and improve the success rate of killing well. This paper established a novel multi-density dynamic well killing method and the corresponding software, which has been verified with field data and applied in ultra-deep well killing simulating. Through the application, the results show that it can provide technical support to reduce operating risk, and the novel method can improve the success rate of killing well and reduce the drilling cost.
- North America > United States (0.93)
- Asia > Middle East > UAE (0.28)
- Asia > China > Xinjiang Uyghur Autonomous Region (0.24)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.93)
- Asia > China > Sichuan > Sichuan Basin (0.89)
A New Approach of Infill Drilling Optimization for Efficient Transition to Future Pattern Flood Development
Huang, Qingfeng (Abu Dhabi Marine Operating Company) | Arii, Hiroaki (Abu Dhabi Marine Operating Company) | Sadok, Abdel Aziz (Abu Dhabi Marine Operating Company) | Baslaib, Mohamed A. (Abu Dhabi Marine Operating Company) | Sasaki, Akihito (Abu Dhabi Marine Operating Company)
Abstract Infill drilling has been recognized as a common practice to accelerate oil production and increase ultimate recovery. Infill drilling can be performed under different drive mechanisms (primary, secondary and tertiary). With a certain history of development, many oil fields have become mature to some extend with waterflood. In order to have a sustainable corporate development plan, pattern flood towards further EOR is considered. Nonetheless a tertiary process as a whole project involves massive investment with high risks and uncertainties. If incremental oil can be recovered via infill drilling as a transition, the investment can be partially offset and justified. Infill oil producers as components of pattern flooding can be accelerated while pattern water injectors can be scheduled in a latter phase. Two main approaches are used in the determination of infill potential. The first one uses empirical techniques to determine infill wells number and spacing based on volumetric calculation of oil in place. It ignores impact of reservoir heterogeneity and continuity. The second approach relies on numerical simulation coupled with optimization algorithms. Based on the second approach, this paper presents a new one that looks at the remaining mobile oil distribution at the time of infill drilling, and locates the optimum pattern configurations whose centers have the maximum sum of stacked mobile oil thickness of each pattern. Each square pattern has only one oil producer centered without corner water injectors. An automated algorithm has been generated to identify infill potential and locations. First, the remaining stacked mobile oil distribution is calculated; second, multiple average-spacing pattern realizations are placed on the field, and only one realization is chosen since it has the highest value of summing mobile stacked oil thickness; third, remove infill wells which have nearby existing oil producers in the pattern area; then, select perforation intervals with a certain criteria to avoid early water/gas breakthrough; after that, an automatic schedule of infill wells is output for simulation run to screen potential infill wells having minimum impact on the existing wells. This infill drilling approach identifies potential pattern oil producers to recover mobile oil, sustain the production plateau and increase oil recovery, prior to planning pattern water injectors. In offshore field, tower slots are limited, so some infill wells can be utilized to workover/sidetrack future inactive wells to save slots. Infill wells can be coupled utilizing conventional completion strategy to minimize wells count. These wells act as a smooth transition to future pattern configurations towards further EOR to recover remaining oil. For the first time, this paper demonstrates a novel approach of determining infill locations by chasing in-situ stacked mobile oil thickness at the specified time step. An automated program is generated to efficiently identify infill wells at any time step. A complete workflow of infill drilling and transition to pattern flood is prepared for a full image. This process also suits both new and mature field. Pattern flood is accelerated by drilling infill oil producers and followed by water injectors.
- North America > United States > Texas (1.00)
- Asia > Middle East (0.94)
- Africa (0.93)
- North America > United States > Wyoming > Greater Green River Basin > Wamsutter Basin > Wamsutter Field (0.99)
- Asia > Middle East > Oman > Ad Dhahirah Governorate > Arabian Basin > Rub' al-Khali Basin > Block 6 > Lekhwair Field > Thamama Group > Thamama Group > Shuaiba Formation (0.99)
- Asia > Middle East > Oman > Ad Dhahirah Governorate > Arabian Basin > Rub' al-Khali Basin > Block 6 > Lekhwair Field > Thamama Group > Kharaib Formation > Shuaiba Formation (0.99)
Abstract Compartmentalization (vertical and lateral) is often a major uncertainty at the field appraisal stage, impacting important investment decisions. Unfortunately the most definitive compartmentalization data (dynamic production data or high resolution 4D seismic) are not usually available so early in field life. Usually geometrically equal sectors are defined to start with to be used as a guide to monitor and manage the reservoir. In mature reservoirs, these data are available. The understanding of the different zones and compartments boundaries is crucial for better re-sectorization and better effective management strategies especially in case of the giant field with gas and water injection schemes. This paper illustrates how indications of compartmentalization and limits of sectors can be achieved by analyzing the sporadic pressure static data dispersed in time and space. The first step of this work is to integrate seismic re-interpretation of the faults with Bottom-Hole Static Pressure (BHSP) analysis to identify acting faulting and barriers. This is achieved by overlapping all isobar maps and then by taking the derivatives of the pressure versus x and y directions. The pressure contrast in space can be highlighted based on a given threshold. If this pressure contrast is overlapped by a seismically identified fault, it can be considered as a barriers or semi-barrier or just absent based on the degree of pressure contrast. The second step is to use the identified boundaries as guidance to limit and confine the different zones and fault blocks that are suspected to act more or less as isolated units. After identifying the boundaries more or less sealing a classification of the static pressure measurements using k-mean value will help to identify the dynamic regions that behave in the same way. A re-sectorization of the field will be based on those units. A forward calculation of voidage replacement ratio and injection production versus pressure can be used as a criterion for identifying the best sectoring scenario.
- North America > United States (1.00)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.16)
- Geology > Rock Type (0.68)
- Geology > Geological Subdiscipline (0.47)
- Geology > Structural Geology > Fault (0.35)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)