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Results
Whitespace Dashboard: A Digital Solution to Support Production Optimization and Decision-Making Processes by Using Field Deliverability Forecasts and Planned/Unplanned Field Activities
Suieshova, Alina (Tengizchevroil LLP) | Seitmaganbetov, Nurzhan (Tengizchevroil LLP) | Kurimov, Dauren (Tengizchevroil LLP) | Benther, Arnoldo (Tengizchevroil LLP) | Nadirov, Maruan (Tengizchevroil LLP) | Bekov, Samat (Tengizchevroil LLP) | Mukhtassyrova, Akmaral (Tengizchevroil LLP) | Yergaziyev, Omirbek (Tengizchevroil LLP) | Andabek, Zanggarbay (Tengizchevroil LLP) | Omirbekova, Kamila (Tengizchevroil LLP) | Suleimenov, Olzhas (Tengizchevroil LLP) | Orazov, Baurzhan (Tengizchevroil LLP) | Kuatov, Asan (Tengizchevroil LLP)
Abstract For most of the life of Tengiz oil field, production has been constrained by the processing capacity of the existing plant facilities, therefore, the excess capacity of oil production above facility capacities (so called whitespace) has been used to compensate losses while conducting maintenance, reservoir surveillance and other field activities without impact on overall production. This paper describes an improved production forecasting approach to optimize field activities planning. The initial forecasting methodology required manual inputs. These manual set-up of models for predicting future production is time consuming and can sometimes be subject to human errors. Therefore, it was decided to automate the process and move from an Excel-based tool to digital solutions. The WhiteSpace Dashboard is a web-based tool designed to optimize and automate inputs for Integrated Production Modelling (IPM) and improve the decision-making process, while maximizing production deliverability to the plants. The tool embodies a set of automated workflows that increase the accuracy of production modeling based on automated data inputs, additional functionalities, data quality checks, ability to run several simulation cases on a cloud server and ability to compare the results. The new dashboard also aids to minimize human error and allows QA/QC of input and output data and parameters. The development of a fit-for-purpose digital tool, the WhiteSpace Dashboard, allowed TCO to combine all existing systems of record into one database with the opportunity to integrate various activities into one set of data input for production forecasting. All the subsurface data required for production forecasting is also automatically pulled from the Digital Oil Field system of record with functionality to change the values for subsurface data such as Gas-Oil-Ratio (GOR), reservoir pressure and productivity index inside the web interface. Users have the flexibility to set up several simulation cases, run them in queues, view the status of the runs, and analyze the simulation results any time after completion. The output of the production modeling is the production forecast, and users have all the required functionalities to analyze each scenario in details. Additionally, users can compare several cases on a single chart. The developed tool significantly improves selection of the best schedule for operational activities to minimize risks of production losses, optimize production and meet production targets. This paper aims to provide an overview of the integrated production forecasting tool and to share the best practices in the planning of operational activities in the field with optimized schedule and minimized impact on overall field production.
- Asia > Kazakhstan > Mangystau Region (0.89)
- North America > United States > Texas > Coleman County (0.24)
- Asia > Kazakhstan > West Kazakhstan > Precaspian Basin (0.99)
- Asia > Kazakhstan > Mangystau Oblast > Precaspian Basin > Tengiz Field > Tengiz Formation (0.99)
- Asia > Kazakhstan > Mangystau Oblast > Precaspian Basin > Tengiz Field > Korolev Formation (0.99)
- (4 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Quality (0.54)
The Best Scenario for Geostatistical Modeling of Porosity in the Sarvak Reservoir in an Iranian Oil Field, Using Electrofacies, Seismic Facies, and Seismic Attributes
Mehdipour, Vali (Department of Petroleum Engineering, Amirkabir University of Technology) | Rabbani, Ahmad Reza (Department of Petroleum Engineering, Amirkabir University of Technology (Corresponding author)) | Kadkhodaie, Ali (Earth Sciences Department, Faculty of Natural Science, University of Tabriz)
Summary The lateral and vertical variations in porosity significantly impact the reservoir quality and the volumetric calculations in heterogeneous reservoirs. With a case study from Iranโs Zagros Basin Sarvak reservoir in the Dezful Embayment, this paper aims to demonstrate an efficient methodology for distributing porosity. Four facies models (based on electrofacies analysis data and seismic facies) with different geostatistical algorithms were used to examine the effect of different facies types on porosity propagation. Both deterministic and stochastic methods are adopted to check the impact of geostatistical algorithms on porosity modeling in the static model. A total of 40 scenarios were run and validated for porosity distribution through a blind test procedure to check the reliability of the models. The studyโs findings revealed high correlation values in the blind test data for all porosity realizations linked to seismic facies, ranging from 0.778 to 0.876. In addition, co-kriging to acoustic impedance (AI), as a secondary variable, increases the correlation coefficient in all related cases. Unlike deterministic algorithms, using stochastic methods reduces the uncertainty and causes the porosity model to have an identical histogram compared with the original data. This study introduced a comprehensive workflow for porosity distribution in the studied carbonate Sarvak reservoir, considering the electrofacies, and seismic facies, and applying different geostatistical algorithms. As a result, based on this workflow, simultaneously linking the porosity distribution to seismic facies, co-kriging to AI, and applying the sequential Gaussian simulation (SGS) algorithm result in the best spatial modeling of porosity.
- Europe (1.00)
- Asia > Middle East > Iran (1.00)
- North America > United States > Texas (0.67)
- Africa > Middle East > Egypt > Nile Delta (0.28)
- Phanerozoic > Mesozoic > Cretaceous > Upper Cretaceous (0.46)
- Phanerozoic > Mesozoic > Cretaceous > Lower Cretaceous (0.46)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.93)
- Geology > Sedimentary Geology (0.93)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.46)
- South America > Ecuador > Oriente Basin (0.99)
- South America > Ecuador > Napo > Oriente Basin > Napo Formation > Napo T Formation (0.99)
- Oceania > Australia > Western Australia > Perth Basin (0.99)
- (18 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Data Science > Data Mining (0.93)
- Information Technology > Modeling & Simulation (0.88)
Pressure Transient Analysis for Water Injection Wells with Waterflooding-Induced Nonsimultaneously Closed Multistorage Fractures: Semianalytical Model and Case Study
Wang, Zhipeng (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing)) | Ning, Zhengfu (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing) (Corresponding author)) | Guo, Wenting (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing)) | Lu, Weinan (China Petroleum Engineering Construction Corporation) | Lyu, Fangtao (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing)) | Liu, Gen (Beijing Institute of Technology)
Summary Waterflooding will induce the opening and extension of fractures, which will create some water flow channels. Due to fracture multiclosures, the obtained fracture half-length from conventional finite-conductivity models is less than the actual value, leading to water flow channels that have been formed but not detected by engineers. According to a large number of waterflooding-front matching schematics and interwell connection coefficient analyses, we find that waterflooding usually connects natural fractures to form bi-induced fractures, which will close nonsimultaneously during the falloff test. In this paper, we develop a waterflooding-induced nonsimultaneously closed multistorage fracture model (WNMF) to describe waterflooding-induced fracture characteristics accurately. The bi-induced fractures are separated into multiple segments to calculate their pressure response. The closed induced-fracture conductivities are constant, and the opened induced-fracture conductivities follow the exponential equation measured by the experiments. Induced-fracture interference and multistorage effects are considered. Finally, the Duhamel principle is used to characterize the storage effects of bi-induced fractures and the wellbore. Results show that the type curve of the WNMF model has bi-peaks on the pressure derivative curve, which was regarded as error data in the past. Closed induced-fracture half-length is identified quantitatively. We can obtain an induced-fracture angle by matching the interference flow (an innovative flow regime in this paper), which can guide engineers to prevent and monitor water breakthrough in time. Using the obtained parameters (induced-fracture angle and closed induced-fracture half-length) can guide well pattern encryption and reasonable well location determination. If the induced-fracture angle is 90ยฐ, an additional horizontal line will be shown on the pressure derivative curve. When the horizontal line is misidentified as a quasiradial flow regime, the obtained reservoir permeability will be amplified many times. The multistorage coefficient is obtained to correct the magnified storage coefficient. Equation calculation and model matching methods verify each other to improve closed induced-fracture half-length accuracy. In conclusion, the experiment and mathematical model methods work together to describe the pressure response behavior of water injection wells. The WNMF model is compared with the conventional finite-conductivity model to verify its accuracy. A field case demonstrates its practicality.
- Europe (1.00)
- Asia > China (0.68)
- North America > United States > Texas (0.28)
- (2 more...)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.61)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > Block 23/27 > op (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > Block 23/22a > op (0.99)
- Asia > China > Shanxi > Ordos Basin > Changqing Field (0.99)
- (4 more...)
- Information Technology > Data Science (0.46)
- Information Technology > Modeling & Simulation (0.34)
Subsurface-Guided Production Surveillance for High-Confidence Operational Decisions
Khan, Osama Hasan (SLB, Abingdon, Oxfordshire, United Kingdom) | Gurpinar, Omer (SLB, Denver, Colorado, United States of America) | Banerjee, Raj (SLB, Houston, Texas, United States of America) | Kano, Daniel Pupim (SLB, Abingdon, Oxfordshire, United Kingdom) | Tellez, Camillo (SLB, Quito, Pichincha, Ecuador) | Suarez, Gabriel Gil (SLB, Quito, Pichincha, Ecuador) | Grijalva, Ricardo (SLB, Quito, Pichincha, Ecuador) | Ali, Samad (SLB, Abingdon, Oxfordshire, United Kingdom)
Abstract The surveillance team in an oilfield has the difficult task of maximizing hydrocarbon production while delaying water production to achieve optimum profitability. For instance, in a waterflooded asset, it needs to intelligently allocate the available injection water to achieve a balanced sweep of oil across the reservoir. A sound understanding of the subsurface flow and inter-well communication is essential here, but the team rarely has access to high-fidelity tools that can help them understand the reservoir behavior. Reservoir simulation models encapsulate all the acquired data along with the interpretations of the subsurface teams and are thus ideal tools to base such decisions on but are seldom used in operations as the associated workflows do not conform to the fast decision-making timeframe. This paper presents a system that leverages cloud scalability, automation, and data analytics to extract insights from subsurface models and generate timely operational advice. The solution connects subsurface models with real-time production data through a cloud-based data platform to automate the update of models with the latest production data. An optimizer is employed that uses streamline-based properties to determine the optimum operating settings for the injection and production wells. The optimization objective can be tailored to align with the asset management goals, such as reducing water recycling and balancing recovery or voidage across the field. The outputs from the subsurface model are translated into actionable insights through a dashboard of fit-for-purpose analytics that presents operational recommendations along with the forecasted outcomes. The system also performs a series of domain-derived confidence checks on the model to quantify the reliability of the recommendations generated. A virtual field management framework is used that captures all the field operating constraints. The entire workflow is automated and can be scheduled to run at a defined frequency so that the surveillance team always has access to proposed actions based on the latest production conditions. To further accelerate the time to decision, machine learning-based avatars of the full subsurface model and reduced-order representations can be integrated into the framework. A case study is presented that describes the application of this subsurface model-driven operational optimization system to a field in the Amazon basin, South America. Using the solution, the subsurface modeling, production surveillance, and operations teams were able to work together to identify opportunities for reducing water recycling and increasing oil production while considerably accelerating the decision-making process due to automation and focused analytics. The paper demonstrates how the latest digital technologies have removed the barriers to the use of detailed subsurface models in guiding operations. The framework described can be used to improve the operational decision-making in any hydrocarbon asset regardless of the recovery mechanism.
- North America > United States > Texas (0.94)
- South America (0.86)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.16)
- South America > Ecuador > Orellana > Amazon Basin (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- (28 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science (1.00)
- Information Technology > Communications > Networks (1.00)
- (2 more...)
Rapid Model Update - Enabling Fast, Structured Dynamic Model Updates Leveraging Automation
Su, S. J. (SLB, Abu Dhabi, United Arab Emirates) | Tahir, S. (SLB, Abu Dhabi, United Arab Emirates) | Ramatullayev, S. (SLB, Abu Dhabi, United Arab Emirates) | Kloucha, C. Kada (ADNOC Upstream, Abu Dhabi, United Arab Emirates) | Mustapha, H. (SLB, Abu Dhabi, United Arab Emirates)
Abstract The quality of a reservoir simulation model is essential for reliable field development planning. To reduce uncertainties on production forecast, the data integrated into the model should incorporate all wells drilled along with their completion data and historical production and injection data and reflect accurately the latest understanding of the subsurface based on all the latest measurements performed in the field. Additionally, the model should be able to reproduce the historical production and injection to a good accuracy. To improve the reservoir engineersโ ability to keep the reservoir simulation models up-to-date, a fully automated framework was developed to facilitate the process of gathering data and reviewing the quality of the dynamic reservoir model by connecting directly to production databases from the geomodelling platform, integrating all required data for dynamic reservoir modelling, and providing automated data quality check tools to assess the quality of the input data to the simulation model, as well as the quality of the simulation results against historical data. The framework enables a rapid update of the static modelling data, relative permeability and capillary pressure data, fluid model and initial conditions of the reservoir. Additionally, a direct channel to production databases (e.g., Oracle database, OFM database) is open to retrieve the latest well trajectories, completion data, and associated historical measurements to ensure that the latest available data is integrated with the model without introducing human errors through manual data manipulation. Additionally, provides quality control tools enable checking the consistency in multiple building blocks of the dynamic reservoir model, such as the compatibility of drainage and imbibition relative permeability curves and capillary pressure curves used in modelling hysteresis processes, potential issues in black oil fluid model tables. A streamlined process to evaluate the quality of the model calibration to historical data is also integrated and provides flexible metrics and visualization to assess the quality of the history match of standard properties such as production/injection rates, cumulative production/injection volumes, water cut, gas-oil ratio, static and flowing pressures, and enables the assessment of the model quality against additional advanced metrics such as water breakthrough time, and match to surveillance logs (e.g. saturation logs, pressure logs, production logs). The framework was applied on a giant onshore carbonate oilfield with a large well count. Quality control was performed to assess the consistency of the reservoir simulation model input data against production databases, followed by an update of the model and the assessment of the quality of the history match following the update. The process that the engineer could spend weeks to perform can now be performed in days, with a higher quality and accuracy through automation, with a fast understanding of the model quality to perform reliable field development plan optimization.
- North America (0.93)
- Asia > Middle East > UAE (0.48)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Quality (0.69)
- South America > Brazil (1.00)
- South America > Argentina (1.00)
- Oceania > Australia (1.00)
- (39 more...)
- Summary/Review (1.00)
- Research Report (1.00)
- Personal > Honors (1.00)
- (6 more...)
- Phanerozoic > Paleozoic (0.67)
- Phanerozoic > Cenozoic (0.67)
- Geology > Sedimentary Geology > Depositional Environment (1.00)
- Geology > Mineral (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- (8 more...)
- Transportation (1.00)
- Media (1.00)
- Materials > Metals & Mining (1.00)
- (14 more...)
- South America > Colombia > Meta Department > Llanos Basin > Cano Sur Block > Carbonera Formation (0.99)
- South America > Brazil > Campos Basin (0.99)
- South America > Argentina > Patagonia > Neuquรฉn > Neuquen Basin (0.99)
- (105 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
- (17 more...)
Ensemble Machine Learning for Data-Driven Predictive Analytics of Drilling Rate of Penetration (ROP) Modeling: A Case Study in a Southern Iraqi Oil Field
Al-Sahlanee, Dhuha T. (BP) | Allawi, Raed H. (Thi-Qar Oil Company) | Al-Mudhafar, Watheq J. (Basrah Oil Company) | Yao, Changqing (Texas A&M University)
Abstract Modeling the drill bit Rate of Penetration (ROP) is crucial for optimizing drilling operations as maximum ROP causes fast drilling, reflecting efficient rig performance and productivity. In this paper, four Ensemble machine learning (ML) algorithms were adopted to reconstruct ROP predictive models: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boost (XGB), and Adaptive Boosting (AdaBoost). The research was implemented on well data for the entire stratigraphy column in a giant Southern Iraqi oil field. The drilling operations in the oil field pass through 19 formations (including 4 oil-bearing reservoirs) from Dibdibba to Zubair in a total depth of approximately 3200 m. From the stratigraphic column, various lithology types exist, such as carbonate and clastic with distinct thicknesses that range from (40-440) m. The ROP predictive models were built given 14 operating parameters: Total Vertical Depth (TVD), Weight on Bit (WOB), Rotation per Minute (RPM), Torque, Total RPM, flow rate, Standpipe Pressure (SPP), effective density, bit size, D exponent, Gamma Ray (GR), density, neutron, and caliper, and the discrete lithology distribution. For ROP modeling and validation, a dataset that combines information from three development wells was collected, randomly subsampled, and then subdivided into 85% for training and 15% for validation and testing. The root means square prediction error (RMSE) and coefficient of correlation (R-sq) were used as statistical mismatch quantification tools between the measured and predicted ROP given the test subset. Except for Adaboost, all the other three ML approaches have given acceptable accurate ROP predictions with good matching between the ROP to the measured and predicted for the testing subset in addition to the prediction for each well across the entire depth. This integrated modeling workflow with cross-validation of combining three wells together has resulted in more accurate prediction than using one well as a reference for prediction. In the ROP optimization, determining the optimal set of the 14 operational parameters leads to the fastest penetration rate and most economic drilling. The presented workflow is not only predicting the proper penetration rate but also optimizing the drilling parameters and reducing the drilling cost of future wells. Additionally, the resulting ROP ML-predictive models can be implemented for the prediction of the drilling rate of penetration in other areas of this oil field and also other nearby fields of the similar stratigraphic columns.
- North America > United States > Texas (1.00)
- Asia > Middle East > Iraq > Basra Governorate (0.68)
- Africa > Middle East > Algeria (0.68)
- Europe (0.68)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.66)
- North America > United States > Texas > East Texas Salt Basin > Forest Hill Field > Harris Sand Formation (0.99)
- North America > United States > Louisiana > East Texas Salt Basin (0.99)
- Asia > Vietnam > South China Sea > Cuu Long Basin > Block 9-2 (0.99)
- (10 more...)
- Well Drilling > Drilling Operations > Drilling optimization (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- (2 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Mining (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- (2 more...)
Case Studies and Operation Features of Long Horizontal Wells in Bazhenov Formation
Yushchenko, T. S. (GazpromneftโTechnological Partnerships (Corresponding author)) | Demin, E. V. (GazpromneftโScience and Technology Center) | Khabibullin, R. A. (GazpromneftโScience and Technology Center) | Sorokin, K. S. (GazpromneftโTechnological Partnerships) | Khachaturyan, M. V. (GazpromneftโPalyan) | Baykov, I. V. (GazpromneftโTechnological Partnerships) | Gatin, R. I. (GazpromneftโTechnological Partnerships)
Summary In this study, unique field data analysis and modeling of operating wells with an extended horizontal wellbore (HW) and multistage hydraulic fracturing (MHF) in the Bazhenov formation were conducted. Moreover, a large amount of long horizontal well data obtained from the Bazhenov formation field was used. Wells with extended HW drilling and MHF are necessary for commercial oil production in the Bazhenov formation. Problems can occur in such wells when operating in the flowing mode and using an artificial lift at low flow rates. This study aimed to describe the field experiences of low-rate wells with extended HWs and MHF and the uniqueness of well operations and complexities. It was also focused on modeling various operation modes of such wells using specialized software and accordingly selecting the optimal downhole parameters and analyzing the sensitivity of fluid properties and well parameters to the well flow. The flow rates in wells with extended HW and MHF decrease in the first year by 70โ80% when oil is produced from ultralow-permeability formations. Drainage occurs in a nonstationary mode in the entire life of a well, leading to complexities in operation. A comprehensive analysis of field data [downhole and wellhead pressure gauges, electric submersible pump (ESP) operation parameters, and phasesโ flow rate measurements] and fluid sample laboratory studies was conducted to identify the difficulties in various operating modes. For an accurate description of the physical processes, various approaches were used for the numerical simulation of multiphase flows in a wellbore, considering the change in the inflow from the reservoir. The complexities that may arise during the operation of wells were demonstrated by analyzing the field data and the numerical simulation results. The formation of a slug flow in low flow rates in a wellbore was caused by a rapid decline in the production rate, a decrease in the water cut, and an increase in the gas/oil ratio (GOR) over time. Based on the results, proppant particles can be carried into the HW and thereby reduce the effective section of the well in case of high drawdowns in the initial period of well operation. Consequently, the pressure drops along the wellbore increased, and the drawdown on the formation decreased. Other difficulties were determined to be associated with the consequences and technologies of hydraulic fracturing (HF). These effects were shown based on the field data and the numerical simulation results of the flow processes in wells. In addition, corrective measures were established to address various complexities, and the applications of these recommendations in the field were conducted.
- Asia > Russia (1.00)
- North America > United States > Texas (0.68)
- South America > Argentina > Neuquรฉn Province > Neuquรฉn (0.28)
- South America > Argentina > Patagonia > Neuquรฉn > Neuquen Basin > Vaca Muerta Shale Formation (0.99)
- South America > Argentina > Patagonia > Neuquรฉn > Neuquen Basin > Vaca Muerta Field > Vaca Muerta Shale Formation (0.99)
- Asia > Russia > West Siberian Basin > Bazhenov Formation (0.99)
- Information Technology > Modeling & Simulation (0.54)
- Information Technology > Data Science (0.48)
Abstract The past few years have been challenging for the oil and gas industry. Many processes and operations have needed to adapt to lower oil and gas prices, caused in part by the COVID-19 pandemic. Understanding reservoir producibility and proving reserves are keys to generating a reservoir field development plan (FDP). However, the different processes to obtain such answers are strongly dependent on cost. The value of information is an extremely important criterion for operators to decide whether to proceed with their discoveries. In an interval pressure transient test (IPTT), a formation tester is used to pump a fluid from a single point or small interval of the formation into the wellbore. Zones of interest can be isolated and tested separately zone by zone. Mud filtrate and reservoir fluids are pumped continuously using the downhole pump, and a downhole fluid analyzer (DFA) is used to monitor the fluid cleanup process. The post-pumping p pressure buildup can be analyzed in a similar manner to traditional well test analysis. Such IPTT have been available since 1980s; however, comparisons of IPTT to actual well tests and other permeability measurements were rarely published until the early 2000s. IPTT have been widely used in the past 20 years, especially in combination with dual packers, and more recently with single packers. Operation efficiency and safety have improved significantly. However, interpretation of the pressure transient obtained from an IPTT is not always well understood. Frequently asked questions (FAQs) include the following: What is an IPTT or a vertical interference test (VIT)? How does an IPTT compare with other permeability measurements? What are the different scales of pressure transient data? How do we upscale zone permeability to an entire reservoir interval? What is next? This paper will address these questions using both reservoir simulation and field data. The field examples are from different environments, ranging from shallow marine to turbidite to deepwater environments, with different fluid systems, such as black oil, heavy oil, waxy oil, gas, and gas condensate. Geographically, the field data include examples from South East Asia and the Middle East. Permeability obtained from pretests, IPTT, nuclear magnetic resonance (NMR), core analyses, and well testing will be compared. Recently deep transient testing (DTT) has been introduced in the industry. With DTT, we can flow faster and longer than previously possible with formation testers, enabling pressure transient analysis in higher permeability and thicker formation. Further data quality improvements come from new, high-resolution gauges deployed with an intelligent wireline formation testing platform. This paper includes a review of the DTT method with several field examples. Finally, the advantages and disadvantages of the different testing methods are discussed relative to the test objectives, with the intent to provide a cost-effective data selection method to ensure sufficient FDP input and to justify the value of investment to the relevant stakeholder.
- Europe (1.00)
- North America > United States > Texas (0.46)
- Asia > Middle East > UAE (0.28)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Pressure transient analysis (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Quality (0.54)
Abstract Sand production erodes hardware, blocks tubulars, creates downhole cavities, and must be separated and disposed of on the surface. Completion methods that allow sand-prone reservoirs to be exploited often severely reduce production efficiency. The challenge is to complete wells to keep formation sand in place without unduly restricting productivity. To avoid sands, the dependence of rock failure on drawdown must be analyzed properly using geomechanical methods. A wide range of methods has been used in the past for sand production prediction. Modeling and designing bottom hole features, mostly perforation geometry and wellbore trajectory, based on sand body mechanical properties and near wellbore stresses is the key to mitigating or eliminating sand production issues in clastic reservoirs. In this paper, we present a full-scale geomechanical analysis and modeling using a 1D mechanical earth model (elastic and strength properties and stress field) to address the main challenges and the root causes of sanding in clastic reservoirs in South of Iraq. The analysis includes the following: Critical Draw Down Pressure (CDPP) profile in different levels of reservoir depletion resulting in sand production. CDPP is the minimum borehole pressure for which no solids are produced from a sand reservoir. Well inclination sensitivity analysis and its role in sand production Sand grain size sensitivity analysis and its role in sand production Unconfined compressive strength (UCS) as rock strength indicator sensitivity analysis and its role in sand production Perforation direction sensitivity analysis relative to stress directions, and its role in sand production. We present a case study to show how the methodology and workflows have been applied to identify sand-producing intervals for different completion scenarios, determine the optimum drawdown conditions for sand-free production and adjust completion configuration to obtain sand-free production from a clastic reservoir in the South of Iraq.
- Asia > Middle East > Iraq (0.46)
- North America > United States > California (0.28)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.35)
- Information Technology > Modeling & Simulation (0.35)
- Information Technology > Data Science > Data Mining (0.34)