For waterflood management and production optimization purposes, producers and injectors are often grouped into pattern elements based purely on the wells' location. This grouping is often done manually and can be time consuming. Also, for irregular patterns is it not always obvious which wells can be grouped together. The paper presents an algorithm that formalizes the grouping and automatically identifies pattern elements.
The presented algorithm focuses on identification of pattern elements that contain one producer and several neighboring injectors. First, using the wells' coordinates as input data, Voronoi polygons are constructed. Based on the well types (producer or injector) and common edges of Voronoi polygons producer-injector pairs are identified. Pattern elements are constructed by splitting Voronoi polygons of injectors and adding their parts to Voronoi polygons of producers from correspondent wells pairs. In the resulting pattern elements injectors are located in one of the vertices while producers lie inside the element.
The presented algorithm was tested on synthetic examples and proved to identify pattern elements for both regular (e.g. line drive, 5-spot, etc.) and irregular waterflood patterns. Therefore, it is suitable for automated analysis of waterflood patterns in cases where data or time constraints do not allow to implement streamline analysis or other sophisticated techniques. Calculation of voidage replacement for each identified pattern element allows to reveal areas of over-injection as well as areas lacking pressure support, and accordingly adjust rate targets for each injector in order to achieve and maintain a balanced waterflood. Automated identification of waterflood pattern elements helps to timely adjust water injection when new producers come on stream or existing wells shut-in.
The proposed method provides a fast and pragmatic approach of identifying and updating patterns without performing dynamic simulation. Formalized steps of the presented algorithm allow automated identification of waterflood pattern elements. In addition to manual labor savings, it avoids subjectivity and ambiguity that can arise in case of irregular patterns.
Saroj, Vikram Singh (Petroleum Development Oman) | Al Zadjali, Faris Said Ali (Petroleum Development Oman) | Calvert, Stephen John (Petroleum Development Oman) | Al Hattali, Ahmed Salim (Petroleum Development Oman) | Al Rawahi, Mohamed (Petroleum Development Oman) | Hussain, Abid (Petroleum Development Oman) | Al Kharusi, Dawood (Petroleum Development Oman)
This paper discusses the further development of Burhaan West Field, a complex multilayered onshore tight gas reservoir that is one of the largest in the Sultanate of Oman. After several years of production through vertical comingled fractured wells, the foreseen decline below production target triggered an integrated assessment of the field. After considering various subsurface development and surface evacuation options, an opportunity for further field development at minimum cost was identified and selected. The integrated assessment of the field for further development optimization included the following work-streams: Interdisciplinary data analysis to determine the critical elements of the recovery process. Building a range of integrated models capturing the subsurface complexity and diversity of rock properties. Optimized well type and spacing which focused on the advantages of infill drilling for improved aerial/vertical drainage. Phased development along with de-risking of the newly proposed areas. Decision based integrated production modelling to screen various evacuation options. Cost optimization The development of a Well Reservoir and Facility Management (WRFM) strategy.
Interdisciplinary data analysis to determine the critical elements of the recovery process.
Building a range of integrated models capturing the subsurface complexity and diversity of rock properties.
Optimized well type and spacing which focused on the advantages of infill drilling for improved aerial/vertical drainage.
Phased development along with de-risking of the newly proposed areas.
Decision based integrated production modelling to screen various evacuation options.
The development of a Well Reservoir and Facility Management (WRFM) strategy.
The proposed optimized field development enhances the field gas production capacity by 50%, while increasing ultimate recovery by 24%. This is achieved at low surface development cost, utilizing existing facilities, through infill drilling in the Core area and development of the Extension area. The conducted work highlighted the following key aspects of developing a tight gas reservoir: Integrated cross-discipline data analysis is required to identify the critical elements contributing to gas and condensate recovery processes. In the Burhaan Field, this has revealed the presence of key marginally resolvable to sub-seismic features that were not previously identified. Integrated Assessment (Integrated Production Modelling) enables for robust and quick evaluation of a variety of surface development options (e.g. evacuation routes and capacity) that is a key in achieving significant project cost optimization. Large gas field developments generally benefit from a phased development approach, where newly proposed areas can be de-risked while high confidence areas are being developed. A comprehensive WRFM plan is a key component of field development. This plan focuses on the activities required to address the field specific uncertainties and associated risks. It needs to be strictly implemented to ensure the delivery of promised volumes.
Integrated cross-discipline data analysis is required to identify the critical elements contributing to gas and condensate recovery processes. In the Burhaan Field, this has revealed the presence of key marginally resolvable to sub-seismic features that were not previously identified.
Integrated Assessment (Integrated Production Modelling) enables for robust and quick evaluation of a variety of surface development options (e.g. evacuation routes and capacity) that is a key in achieving significant project cost optimization.
Large gas field developments generally benefit from a phased development approach, where newly proposed areas can be de-risked while high confidence areas are being developed.
A comprehensive WRFM plan is a key component of field development. This plan focuses on the activities required to address the field specific uncertainties and associated risks. It needs to be strictly implemented to ensure the delivery of promised volumes.
This case study shares the insights on the challenges faced in developing multi-layered tight gas fields. It highlights how development decisions need to be governed by field specific characteristics that can be identified through multi-disciplinary integrated data analysis. The paper also provides an example of an effective Production Modelling workflow to screen through surface development options and demonstrates how focused data acquisition and specific WRFM activities can be embedded into tight gas developments.
Global oil demand has led to the development of new smarter drilling, completion, reservoir management technique and technology to optimize reservoirs production. The production of Kuwait Oil Company (KOC) has reached 3 MMBOPD and KOC’s 2030 vision is to boost the production to 4 MMBOPD. In order to achieve this vision, KOC has started several technical projects and development plans. One of these projects is the North Kuwait Integrated Digital Oil Field (NK-KwIDF) a full-fledged Field project implemented in KOC.
This Paper will discuss the scale, complexity, technology used, and advantage of using the NK-KwIDF. The North Kuwait (NK) asset has five fields, around twelve hundred active wells, and seven Gathering Centers (GCs). A complex network of pipeline, trunk line, and manifold are used to connect these twelve hundred wells to GCs. In order to optimize the production from NK every barrel of production opportunity has to be considered by optimizing suitable wells and minimizing downtime from each field, resulting the development of an extensive surface network model. The extensive surface network model takes into consideration of each and every details of field e.g. pipelines, manifolds, details of GCs and wells. For each and every well in NK assets a well model is prepared considering all PVT parameters, completions, and surface co-ordinate and finally connected to surface network model with all piping information.
Once the extensive surface model was prepared, several integrated workflows were developed in order to efficiently run the surface model and analyze the output from the run. Some of these workflows are ESP Optimization and ESP Analysis workflows, which have capability to identify the Oil Gain Opportunities and diagnose ESP performance. The identify opportunities are logged into ticketing system, which monitors the life cycle of the opportunity right from the identification till implementation into the field for Oil Gains.
The full-fledged development of NK-KwIDF took almost 3 years from the day it was started, as a pilot project with 133 wells. When an excellent result in terms of production optimization and downtime minimization was recorded from the pilot project, the pilot project was expanded to full-fledged field project. The NK-KwIDF project gave an outstanding result of Oil gain from well level as well as Network level optimization. It established an excellent reputation in the oil industry where it was a source of attraction for many NOC’s and IOC’s to visit and follow the flag ship for their development and implementation of digital field technology.
The need for monitoring individual well production in unconventional fields is rising. The drivers are primarily related to accurate reporting for production allocation between wells. The main driver in North American operations for a meter-per-well flow rate monitoring has been the need for accurate per well production accounting due to the complexity of the land-owner interest.
There are additional benefits from the monitoring of early decline and determination of the transient evolution of the reverse productivity index (RPI) to evaluate the well performance. The availability of long-term rate transient data supports decline analysis and rate transient analysis, leading to better understanding of the estimated ultimate recovery (EUR), which may drive the selection of infill drilling locations. Finally, the identification of interference between flowing wells can help mitigate the issues of parent/child wells.
A specific case in the Eagle Ford is the systematic deployment of full gamma-spectroscopy multiphase flowmeters at well pads. This intelligent pad architecture consists of one multiphase flowmeter per well and a production manifold that enables commingling of the production to a single flowline connected to the inlet manifold of the production facility.
The rationale of the decision for the installation of such solution in lieu of a metering separator per well is based on the evaluation of the impact of this technology on capex and opex reductions.
Several lessons learned are provided. They include a discussion of the change management issues related to the installation of the meters, the modifications necessary to the production facility at the receiving side, and the data management and data analytics that were enabled from the gathering of systematic, continuous, and high-resolution measurements.
The impact of the installation of the meters in the field is noticeable and quantifiable. with several prior wells used as a benchmark. The effects are not limited to cost reduction, but also lead to an increase in production related to the release of operational crews from daily well testing tasks that used to be necessary. The data quality and coverage are also increased.
A few suggestions are made concerning optimization of the deployment and use of remote monitoring options for enhanced efficiency. Automated data workflows are also discussed.
The reduction of HSE risks through a better management of field operators is also assessed.
Optimizing steam-assisted gravity drainage (SAGD) performance in oil sands reservoirs relies on the quality of steam allocation decisions made across the well inventory. With finite facility steam generation capacity, SAGD producers are typically challenged with identifying the true opportunity cost of allocating steam volumes based on well performance. This paper presents a novel technique to inform steam allocation decisions and managing SAGD reservoir pressures in service of optimizing production and consequently improving the economic performance of the asset through smarter SAGD field development planning.
The concept of marginal steam-oil-ratio (mSOR) is introduced as a method of guiding steam allocation decisions. Marginal SOR is defined as the cold-water equivalent volume of steam required to produce the next marginal barrel of bitumen from the production system in a steam constrained environment. The metric represents the opportunity cost of deploying a barrel of steam to the next best alternative in steam allocation decisions. Dynamic quantification of mSOR over the plausible range of operating pressures for each producing entity (PRDE) in the inventory (such as a well group or drainage area) is critical to optimally allocating steam when faced with reservoir challenges such as reservoir complexity and heterogeneity and transient reservoir behaviors such as thief zone interaction.
This paper prescribes methodologies to analytically and empirically quantify mSOR for a SAGD production system. Additionally, application of the concept if field production optimization is discussed under the context of integrated production modeling and constrained flow network optimization problems. A case example of applying mSOR to guide steam allocation decisions at ConocoPhillips' Surmont SAGD asset is presented under a steam constrained environment. The mSOR guided solution is validated using brute-force enumeration of steam allocation outcomes in the production system to prove production optimality. The results from this dynamic steam allocation strategy guided by mSOR characterization show significant improvements in field oil rates, field steam management efficiency and consequently the economic value of the SAGD asset.
Recent advances in data acquisition systems have helped in monitoring wells performance and recording their production parameters like pressure, temperature and valve opening in real time with high frequency. A cost-effective technology to estimate well production rates is Virtual Metering, which integrates real time data and analytical models. This paper presents the methodology of an innovative virtual metering tool and the promising results obtained in real case applications on gas, gas condensate and oil fields.
A Virtual Metering tool has been developed by integrating a commercial software platform and mathematical models (algorithms). The algorithms solve simultaneously dynamic pressure and temperature gradients (VLP) along with the choke equation to find the optimal solution rates that match physical sensor readings. Moreover, the tool manages the communication between real time data and the models enabling a safe storage of the results. Models require a manual calibration at reference dates based on well separator tests or MPFM readings, in a way to match total field production. After calibration, the algorithm is able to run automatically in real-time.
Three implementations are presented about gas, gas and condensate and oil fields, showing the benefits and limitations of virtual meter application. Virtual meter proved to be a valid technology with the potential of even replacing MPFM results, especially in dry gas fields. Where MPFM are installed on each wellhead, virtual meter worked as redundant system and allowed to detect precociously flow meters malfunctioning. The allocation workflow has been modified in order to replace MPFM estimations with virtual meter ones. For oil fields with variable production parameters, the tool has provided reliable independent rate estimation by combining VLP and choke calculator in a unique optimization tool. The real time flow rate can be used as a basis for pro-rata allocation of fiscal production in the framework of a Production Data Management System software. Additional features of the tool are the following: a real-time input for pressure and rate transient analysis and a workflow for real-time well drawdown estimation of gas wells, which makes use of automatic p/z reservoir model update to estimate reservoir pressure. Moreover, this tool had a significant impact on production monitoring, improved the effectiveness of production optimization actions and the quality of history match of reservoir 3D model.
This paper contains a novel approach of a reliable and robust virtual metering tool that can be flexibly applied to gas and oil fields through a unique optimization algorithm, which is able to combine information coming from production network and from the reservoir side. It gives benefit to company workflows by feeding external reservoir analysis applications that would not be possible without virtual meter results and uses the results of external applications for validation purpose.
Standard approaches to optimization under uncertainty in reservoir simulation require use of multiple realizations, with variable parameters representing operational constraints and actions as well as uncertain scenarios. We will show how appropriate use of local optimization within the simulation model, using customized logic for field management strategies, can bring improved workflow flexibility and efficiency, by reducing the effort needed for uncertainty iterations.
To achieve meaningful forecasts for an ensemble of uncertain scenarios, it is important to distinguish between different types of decision. Investment decisions, such as facilities sizing, depend on global unknowns and must be optimized for the complete ensemble. Operational actions, such as closing a valve, can be optimized instantaneously for individual scenarios, using measurable information, although subject to constraints determined at a global level. In this study, we implement local optimization procedures within simulation cases, combining customized objective criteria to rank reactive or proactive actions, with the ability to query reservoir flow entities at appropriate frequencies.
The methods presented in the paper can be used for reactive response modeling for smart downhole control; optimization of ESP/PCP pump performance; and implementation of production plans subject to defined downstream limits. For selected cases, we compare the advantages and disadvantages of the local optimization approach with standardized "big-loop" uncertainty workflows. The methodology can significantly reduce optimization costs, particularly for high-frequency actions, achieving similar objective function values in a fraction of the time needed for post-processing optimizers. Use of tailored scripting provides the capability to modernize the logic framework for field management decisions, with realistic representation of smart field equipment and flow entities at any level of complexity.
Use of efficient workflows as described in this paper can reduce the cost of multiple realization studies significantly, or enable engineers to consider a wider range of possible scenarios, for deeper understanding and better risk mitigation.
Masini, Cristian (Petroleum Development Oman) | Al Shuaili, Khalid Said (Petroleum Development Oman) | Kuzmichev, Dmitry (Leap Energy) | Mironenko, Yulia (Leap Energy) | Majidaie, Saeed (Formerly with Leap Energy) | Buoy, Rina (Formerly with Leap Energy) | Alessio, Laurent Didier (Leap Energy) | Malakhov, Denis (Target Oilfield Services) | Ryzhov, Sergey (Formerly with Target Oilfield Services) | Postuma, Willem (Target Oilfield Services)
Unlocking the potential of existing assets and efficient production optimisation can be a challenging task from resource and technical execution point of view when using traditional static and dynamic modelling workflows making decision-making process inefficient and less robust.
A set of modern techniques in data processing and artificial intelligence could change the pattern of decision-making process for oil and gas fields within next few years. This paper presents an innovative workflow based on predictive analytics methods and machine learning to establish a new approach for assets management and fields’ optimisation. Based on the merge between classical reservoir engineering and Locate-the-Remaining-Oil (LTRO) techniques combined with smart data science and innovative deep learning algorithms this workflow proves that turnaround time for subsurface assets evaluation and optimisation could shrink from many months into a few weeks.
In this paper we present the results of the study, conducted on the Z field located in the South of Oman, using an efficient ROCM (Remaining Oil Compliant Mapping) workflow within an advanced LTRO software package. The goal of the study was to perform an evaluation of quantified and risked remaining oil for infill drilling and establish a field redevelopment strategy.
The resource in place assessment is complemented with production forecast. A neural network engine coupled with ROCM allowed to test various infill scenarios using predictive analytics. Results of the study have been validated against 3D reservoir simulation, whereby a dynamic sector model was created and history matched.
Z asset has a number of challenges starting from the fact that for the last 25 years the field has been developed by horizontal producers. The geological challenges are related to the high degree of reservoir heterogeneity which, combined with high oil viscosity, leads to water fingering effects. These aspects are making dynamic modelling challenging and time consuming.
In this paper, we describe in details the workflow elements to determine risked remaining oil saturation distribution, along with the results of ROCM and a full-field forecast for infill development scenarios by using neural network predictive analytics validated against drilled infills performance.
The Prudhoe Bay field, located on the North Slope of Alaska, is the largest oil and gas field in North America. The main Permo-Triassic reservoir is a thick deltaic high-quality sandstone deposit about 500 ft thick with porosities of 15 to 30% BV and permeabilities ranging from 50 to 3,000 md. The field contains 20 109 bbl of oil overlain by a 35 Tcf gas cap. Under much of the oil column area, there is a 20- to 60-ft-thick tar mat located above the oil-water contact (OWC).
A case study on improving waterflood surveillance aided by a better understanding of the correlation between various water injectors and oil producers completed in the shallowest sub-layer of a giant multi-layered matured carbonate reservoir in Mumbai Offshore Basin is presented here. This understanding is then used to gauge effectiveness of the prolonged waterflood programme and to identify ‘target wells’ for optimizing water injection rate. The inferences of this analysis were tested using a simulation model.
Production, injection and pressure data of all wells completed in this sub-layer were extracted. The reservoir injection and withdrawal rates were computed using PVT data which were subsequently fed into an in-house developed streamline simulation program that generates a matrix of flow-based well rate allocation factors (WAF) correlating injection to withdrawal for each individual well as a part of its output. The analysis of injection efficiency per well was carried out in two scenarios viz. with current rates for effective waterflood surveillance and at a cumulative level with averaged rates to identify areas of deficiencies and optimize future injection rates.
Flow-based allocation factors provided a better picture than traditionally employed distance weighted technique owing to the underlying physics involved in describing streamline distribution in the reservoir. Results of analysis at the cumulative level indicated wells where injection efficiency, as measured by the ratio of injection rate to sum of streamlines-weighted withdrawal rates from connected producers, substantially deviates from 1. Few wells had an injector efficiency significantly higher than 1 which defined over-injection and potential recycling while a large number of injector wells had ratios of less than 1, highlighting the need to step-up injection rates and devise strategies for rigorous surveillance. To achieve the latter objective, injection-centric WAF's were regenerated at current situation with current rates and the dynamic nature of these factors could be observed by noting their slight difference with respect to previously estimated factors. This is attributed to averaged-out flow rates limiting the influence of newer high-rate producers and injectors. Nonetheless, wells in areas demanding attention are identified and requisite injection rates are assigned. These changes are included in the history-matched simulation model used for redevelopment activities and results were compared with a do-nothing case. The significant incremental recovery proves as a validation of the methodology adopted.
Waterflood surveillance on a well-to-well basis is always difficult in a matured field where water injectors are deployed in a ubiquitous fashion. This approach has rarely been employed in a reservoir of the size of Mumbai High and can be extended to other sub-layers subject to positive results from field implementation. Thus it is an endeavour to monitor waterflood effectiveness at a large field scale and could be beneficial for similarly developed fields.