Operators have gotten onboard on digital field implementations as a game-changer to optimize oil production, reduce asset development and operation cost by leveraging fields’ real -time data, software solutions, and smart workflow processes. Such implementations have demonstrated improved well placement, well delivery, real-time well monitoring, reduced production loss from unplanned well downtime, and improved HSE.
This paper covers overlooked aspects of digital oil field applications and focus on developing ADHOC solutions pertaining to well/reservoir performance optimization. These solutions will help identify high value opportunities and candidate wells for intervention and workover using operating envelopes, developing robust systems for well integrity and equipment reliability. The solutions will provide transient flow/pressure analyses for reservoir and well performance optimization and provide assessment of well productivity and well system performance. Moreover, the introduced solutions will enable efficient reservoir management and performance analytic tools, well flow capacity validation and optimization (single well/multiple wells). It will also enable horizontal/multi-lateral well monitoring and optimization.
New indices/metrics are introduced to gage the performance and ensure maximum return possible, well architected/subsystem designs, reliability, integrity, and well control issues. These indices are meant to develop life-cycle designs and assure total well management. In addition, to promote operational excellence and improve reliability.
With low oil prices, the industry is challenged to seek opportunities to optimize every producing asset. Engineers are challenged to design the most cost-effective means to recover the greatest amount of production from a well in the shortest amount of time. The real challenge is to simultaneously maximize drawdown and achieve reliability. Digital oil fields have been sought as a platform to attain such objectives. The digital oil field sustainably maximizes production, minimizes CAPEX, OPEX and environmental impact, whilst simultaneously safeguarding the safety of the people involved and the integrity of the associated equipment for the entire upstream production process from reservoir to point of sale. The digital oil field is achieved by integration of rapidly advancing technologies (automation, communication and computing), work processes and people skills/motivation/collaboration, acquiring, then analyzing data to make decisions and take actions at a sufficient frequency to achieve the required gains.
Traditional DOF solutions use a web portal or intranet dashboard as the primary means of data access and collaboration. For an engineer these portals are useful to help identify field problems, reduce the amount of time spent looking for data and enable quicker analysis of issues. However, these platforms do not offer the rich two-way communication environment that could enable real-time interaction between engineers, systems, and support functions. Web portals are generally loosely coupled to modelling and visualization solutions which together can adversely affect performance of the solution. This paper presents how a new approach was tried, tested and proven to offer a transformation in the way we interact with well production, model based analytics and collaborate with others. This paper provides a case study documenting how a foundational Digital Oilfield (DOF) platform was extended to include model based workflows and multi channel mobile device integration.
Summary This case study describes the impact of integrating timelapse (4D) seismic data with reservoir surveillance and production data for the Oveng field area of Okume Complex Field, located offshore Equatorial Guinea, West Africa. Aging assets on production decline can greatly benefit from 4D seismic to determine infill drilling locations as well as support reservoir management practices, but only when the seismic data has been validated against multiple other datasets to produce a robust interpretation. This case study describes a multidisciplinary, collaborative effort towards validating 4D seismic with historical production data along with field surveillance data such as repeat saturation logs, and highlights examples of providing new information for building a more accurate geomodel as well as influencing infill well planning and drilling. The key message is that once all data have been screened and integrated, informed decisions can be made to optimize the value of the asset. Introduction Time-lapse seismic data has proven effective in reservoir management and future production planning (Huang et al., 2011; Gainski et al., 2010; Ebaid et al., 2009; Mitchell et al., 2009; Gonzalez-Carballo et al., 2006).
In this paper, we present a successful implementation for the development and deployment of a system that automates and simplifies the surveillance and optimization workflows for the gas lifted wells at a platform in the Gulf of Mexico. As with other "digital oil field" type systems, the success of the capability deployed, called Gas Lift Optimization Workflows (GLOW), required the use of diverse and more-than-capable library of techniques, algorithms, and methods that already exist in the fields of optimization, machine learning, signal processing, physical modeling, and numerical simulation. We adopted a philosophy in GLOW of relying on our understanding of the underlying physics as much as possible to achieve our goals. We found that most of our problems, like the diagnosis of steady-state gas lift injection through multiple valves or solving for the optimal allocation of gas lift to every well, could be solved to an appropriate level of accuracy with physical models that are automatically matched to well-test and real-time data coming from the field using nonlinear regression techniques.
Ma, Xiang (ExxonMobil Upstream Research Company) | Borden, Zachary (ExxonMobil Upstream Research Company) | Porto, Paul (ExxonMobil Upstream Research Company) | Burch, Damian (ExxonMobil Upstream Research Company) | Huang, Nancy (ExxonMobil Upstream Research Company) | Benkendorfer, Paul (ExxonMobil Upstream Research Company) | Bouquet, Lynne (ExxonMobil Upstream Research Company) | Xu, Peng (ExxonMobil Research and Engineering Company) | Swanberg, Cassandra (ExxonMobil Technical Computing Company) | Hoefer, Lynne (ExxonMobil Technical Computing Company) | Barber, Daniel F. (ExxonMobil Production Company) | Ryan, Tom C. (ExxonMobil Production Company)
A real-time production surveillance and optimization system has been developed to integrate available surveillance data with the objective of driving routine production optimization. The system aims to streamline data capture, automate data quality assurance, integrate high and low frequency data to extract maximum value, optimize the design and analysis of commingled well tests, and provide real-time multi-phase well rate estimates for continuous well performance evaluation.
A key challenge identified was the need to understand individual well contribution during commingled well tests, as traditional approaches may provide unrepresentative results. Additionally, the well tests are typically infrequent, thus further limiting the reliability of estimated well rates as production system dynamics between well tests are not accounted for. A third challenge recognized was the need for efficient testing procedures in order to minimize deferred production. To address these issues, a fully integrated model of the production system was used, and is driven by a computational algorithm that automatically calibrates the model to real-time sensor data.
A new systematic approach was developed to analyze multi-segment commingled well tests simultaneously to improve the accuracy of resulting measurements. Between well tests, a robust regression algorithm is used to continuously adapt and re-calibrate the model when well conditions change. This algorithm can automatically detect sensor bias and apply an appropriate weighting when calibrating the model. In addition, a regularization technique is also used to prevent physically unrealistic changes in the well parameters between infrequent well tests.
The technology is currently applied to an offshore deepwater asset and early benefits include a 2% production uplift realized from optimizing gas lift allocation and performing a single well routing change recommended by the technology. Furthermore, more reliable rate allocation to wells has improved the quality of subsurface models used for reservoir management.
This case study is presented to show the application of Rock Physics Inversion (RPI) as an interpretation tool used in conjunction with other disciplines to better understand the internal reservoir fabric in a weakly confined deepwater slope channel-levee system offshore west Africa. The incorporation of 3D/4D RPI products with other subsurface data assist in providing a better understanding of flow paths and discontinuities within reservoirs as well as refining the calibration of history matching in simulation models.
One of the primary roles of the production subsurface team is to make dynamic models of the reservoir that can be used for production forecasting, reserve determination, and reservoir management. The presence of baffles and barriers associated with the sand distribution impact the behavior within the reservoir of oil production flow, water injection flow and aquifer encroachment. The effectiveness of a water injection program and of reservoir drainage cannot be effectively modeled without first understanding the distribution of the discontinuities of these baffles and barriers in the hydrocarbon bearing zones as well as in the connected aquifers. The Beacon Channel paper discussed by Beaubouef et al. (2011) demonstrated that the ability to history match complex deepwater reservoirs where it is difficult to accurately map the fine details of the reservoir, can be done reasonably well if the major baffle and barrier sites can be modelled correctly. Through the integration of both time lapse difference volumes and 3D/4D RPI data it is possible to identify and map likely barriers in complex deepwater reservoirs.
The feasibility analysis, survey planning, seismic acquisition and time-lapsed processing of a West Africa 4D seismic dataset over the producing Elon Field Area of the Okume Complex, Equatorial Guinea is presented. The survey planning and execution occurred in 2011-2012. Prior to the expenditure of significant funds required for the execution of an ocean bottom cable (OBC) acquisition, several steps were taken to determine the magnitude of the detectable 4D change for this reservoir and to define the optimal design parameters to match the planned OBC Monitor survey the existing Marine Streamer Baseline dataset. In areas where field production infrastructure exists, seismic operations pose significant challenges from the standpoint of safety, as well as for obtaining adequate coverage (in fold, azimuthal coverage and offsets) in key areas near field infrastructure. Differences in source/receiver types, noise levels, and geometry require processing to utilize advanced techniques including deghosting, model-based multiple removal, adaptive noise attenuation and 4D co-binning. The streamer (baseline) and the OBC (monitor) are compared after PSDM and gather conditioning for repeatability using NRMS and time-shift analysis presented. The OBC dataset was also processed independently utilizing the split-spread and wide azimuth component that was removed during the 4D binning process in order to gauge the level of improvement due to the increase in fold and subsequent trace density.
Numerous publications discuss the use of 4D seismic monitoring (4D) and its technical merits as a tool deployed during the life of field, but few address the actual value that the technology brings to a project. This work delineates the incremental economic value brought about by 4D seismic. We identify the contributions of 4D under four broad project categories:
(1) Optimizing Field Development Planning (FDP) through the identification of additional in-field targets. In this sense, 4D is used to extend base production and, in general, it helps arrest field declines by rejuvenating or prolonging the life of a field. Under this category, 4D also adds value by avoiding the drilling of poor producers.
(2) Adjusting Depletion Plans (DP) and understanding production recovery mechanisms as well as their efficiency. When combined with Rock Physics Inversion (RPI), 4D is used to help assess remaining oil saturations and sweep efficiencies during Improved Oil Recovery (IOR).
(3) Utilizing 4D in field surveillance over time to monitor the evolution of the field. This practice will show how well completions can be monitored with 4D.
(4) Managing the life-of-field more effectively, where 4D seismic interpreters are expected to help optimize well costs by specifying optimal well trajectories through the overburden and by shedding light on the conceptual designs for wells with optimized trajectories. In this sense, 4D is used as a tool for protection of reserves and for better management of the field through time.
We highly recommend that the economic assessment of the value of the 4D be undertaken in a systematic manner. This can be done in an Expected Monetary Value (EMV) sense though decision trees. Another way to capture the value of the 4D, is to contrast what the economics and NPV for the field development would be like with and without the 4D.
The uplift in identification and prediction of hydrocarbon sands when using Rock Physics Inversion over Chi angle and Extended Elastic Impedance approaches is presented for a producing asset in Equatorial Guinea, West Africa. Deep water clastic reservoirs with rapid rates of production decline (`30% per year) require continuous phases of infill drilling to fully and effectively deplete the resource. Identification of remaining unswept potential in the fields requires the use of both 3D and 4D seismic data. The 3D data is required for the identification of sedimentary architecture of the reservoir bodies. The 4D seismic is required to identify remaining oil. A comparison of reservoir details obtained from Extended Elastic Impedance (EEI) and Rock Physics Inversion (RPI) are presented. Their relative accuracy in identifying HC sands is quantified and compared. New areas that were derisked using the RPI data are presented. Integrating the 3D inversion products with 4D inversion products has allowed for the identification and quantification of the unswept potential in the field that will form the basis for the next phase of infill drilling in the asset.
This paper presents the results of a joint industry project (JIP) that developed an improved wellbore stability (WBS) tool for wells in three basin areas in the Gulf of Mexico (GoM) and North Sea. The analysis was performed in 106 wells to identify suitable rock failure criteria and rock strength correlation combinations for determining WB Sunder complex geological conditions. A “well quality index” (WQI) parameter was defined for calibrating the shear failure gradient (SFG), taking into account the over-gauge observed and the difference between the SFG and mud weight. In addition, a comparison is made for SFG calculations between an Abaqus finite element method (FEM) and Drillworks® (DWs) Predict Geostress modeling. The analysis showed that the SFGs obtained by these methods are practically identical. However, a three-dimensional (3D) numerical model is more appropriate for WBS analysis in a sub-salt environment because the stress field is strongly perturbed by the presence of salt.
The JIP DEA-161 was performed with the purpose of developing an improvedWBS prognosis tool and evaluating applicable rock failure criteria and rock strength correlations for wells in different basins, as well as determining the minimum limits on an acceptable dataset. Data from 199 wells were collected, and a WBS analysis was performed for 106 wells. The data were acquired in image format from the public domain and digitized as part of the project. The analysis was conducted using limited data. However, it was possible to performa wide variety of approaches and tests. Not all of these were successful, and the lessons learned from the unsuccessful attempts were invaluable in developing an overall successful set of guidelines, methods, and procedures that are incorporated into the results of the study.
2. Project Overview
Five major phases were performed during the project:
i) research to determine current industry practices;
ii) data gathering, quality control, and validation;
iii) determination of in-situ stress and rock strength;
iv) WBS analysis; and
v) documentation and communication of findings.
Figure 1 shows the location of seven fields in the northern GoM shelf and seven in the deepwater GoM basin. A total of 157 wells were analyzed in the two basins, and 76 had sufficient data to perform a full WBS analysis.