this disclosure provides a calm controller system for managing oil and gas field production with a view to optimize profitability . the calm controller system for 4d seismic reservoir management is designed to focus on the need to maximize profitability of the whole at all times and under all uncertainties . the calm controller system integrates the observed 4d seismic differences with a continuously running reservoir simulator to understand the production pathways of fluid withdrawal in each field . the calm controller system includes a feedback loop between actions taken in the field and the return of metrics that score the success or failure of those actions . this feedback is fed into a model that continuously recomputes optimal solutions , strategies or actions to keep the next actions “ always - in - the - money .” such a feedback loop in control system simultaneously optimizes both economic value and operational aspects of “ e - fields ” is called a “ martingale ” controller . the calm martingale controller system (“ calm martingale 4d seisres controller system ”) disclosed herein is an implementation of the general stochastic controller described in anderson et al . u . s . patent application ser . no . 11 / 349 , 711 . the system integrates diverse and multiple software applications and models using a feedback loop . fig1 shows a feedback loop 100 in an exemplary implementation of the calm martingale controller system for 4d seismic reservoir management for optimal profitability . the system generates a strategy ( i . e ., actions ) for optimal profitability of the reservoir through time . the calm controller integrates the observed 4d seismic differences 110 with a continuously running reservoir simulator 120 to understand the production pathways of fluid withdrawal in each field . advantageously , the calm system maximizes profitability even with extreme price fluctuations and geological uncertainties . 1 . 4d analysis of time - lapse seismic changes ( e . g ., amplitude , compressional and shear velocity ) for at least two , but as many as practical , 3d seismic volumes acquired at different times during the production history of a field , and their time - depth conversion , normalization , and differencing ; 2 . well log analysis looking for time - lapse changes in the logs run over several different time intervals in wells and their time - depth conversion ; 3 . reservoir characterization of stacked reservoirs using geostatistical co - kriging ; 4 . exporting of all data , with time - stamps , into the same earth model ; 5 . 3d fluid flow simulation , preferably using finite element modeling ; 6 . 3d seismic modeling , preferably elastic rather than acoustic , to generate synthetic seismic cubes to match the 4d seismic observation time steps ; 7 . differencing of 4d model vs . 4d observed seismic data ( e . g ., difference between model and observed data for a given time ); 8 . analysis of the difference - of - the - differences between the model and observed results ( e . g ., the difference between the predicted - observed differences at two times ); 9 . optimization that identifies changes in physical properties of the reservoirs that are required to match fluid withdrawal , pressure changes , and seismic differences as closely as possible ; and 10 . a continuous “ feedback loop ” to workflow 1 above so that the computation is dynamic . the computations can be continuous ( e . g ., 24 hrs / 7 days a week ) until the field is abandoned . the calm martingale 4d seisres controller system is designed to perform the computational tasks of interacting 3d seismic modeling 130 with statistical reservoir characterization 140 , 4d observed seismic differences , a finite element reservoir simulator 120 , and properties derived from seismic inversion and migration codes 150 . for this purpose , an exemplary implementation of the calm martingale 4d seisres controller has an extensible operating framework ( of ) that enables the interpretation workflow to move easily among various vendor applications needed to complete feedback loop 100 ( fig1 ). the interpretation workflow may include geological and geophysical interpretations , and engineering implementations required for optimal performance by modern e - field asset teams . fig2 shows the architecture of of 200 for the exemplary system implementation of calm martingale 4d seisres controller . of 200 includes major geological , geophysical , and engineering components or subsystems ( e . g ., rock properties , 4d seismic model , 3d reservoir simulator , 4d seismic changes , etc . ), which are managed simultaneously . in of 200 , all actions within any of the many applications of the system or component subsystems are recorded within the of using a web - based action tracker ( hereinafter “ active notebook ”). like a laboratory scientist &# 39 ; s lab book , active notebook allows any previous experiment to be redone . of 200 also includes a vendor - neutral data model with a persistent input / output data repository 210 is required because the data sources for the various users &# 39 ; favorite applications are not likely to be kept in the same data management systems . rapid reviews of present and previous analyses can then be quickly and easily reviewed via versions stored in the data repository . of 200 may provide user - selectable access to various vendors &# 39 ; applications using automated “ wrappers ” ( fig2 ). for example , of 200 may allow a user to choose between popular reservoir simulators ( e . g ., schlumberger ( eclipse ) and halliburton ( vip ) simulators , etc .) at the same time . the wrappers enclose the vendor applications and automatically manage the connectivity , data trafficking , and versioning of inputs and outputs . of 200 includes an “ event handling ” mechanism to make applications run asynchronously ( i . e ., in parallel , but not necessarily at the same time ). the workflow is parsed among the several parallel applications simultaneously and distributed to the client / server network , and then reassembled as it is completed . with this feature , of 200 does not have to wait for one program to finish before beginning another . in exemplary embodiments of the implementation of the calm martingale 4d seisres controller , of 200 / data repository 210 is configured to have a rich set of reusable , extensible “ containers ” to hold engineering , geological , and geophysical data so that new applications and data types can be added to the of management system easily and quickly . in an implementation of the calm martingale 4d seisres controller , of 200 includes an automated meshing system 220 to create the framework needed by the reservoir simulator to build its model from any set of stacked horizons or other geological interpretations such as scanned maps and charts . fig3 shows , for example , of 200 displays of reservoir horizons ( tops and bottoms ) mapped and rendered in a stacked reservoir model . automated meshing system 220 ( e . g ., “ multimesh ” by ibm ) is advantageously “ topological ” so that whatever the grid requirements of an application are , its 3d connectivity can be quickly computed in finite difference , finite element , upscaled , or downscaled versions as needed ( fig4 ). further , of 200 may be provided with a common data viewer ( e . g ., a web - based visualization system ) that crosses applications . the user can then communicate the visualization of progress of his computer simulations over the web as colleagues around the world manipulate the images in real time as they are recomputing ( fig5 a and 5b ). in addition to the of 200 components discussed above , the calm martingale 4d seisres controller system includes an optimizer , which provides parameter optimization services . since the calm martingale 4d seisres controller system is implemented as a loosely coupled , component - based system , the need for parameter estimation varies from application to application and vendor to vendor . the optimizer contains a set of tools that can be deployed at any time and any place within seisres of 200 . this feature effectively provides the user with an “ optimization laboratory or workbench .” the “ optimization workbench ” allows the user a selection of options , including hybrids that combine algorithms of different types , to produce the most appropriate solutions . in practice , the technical goal is to quickly implement sub - optimization loops to facilitate the optimization process for the 4d seismic reservoir simulation . consider application of the calm martingale 4d seisres controller system to an exemplary problem of tracking drainage of oil from a reservoir by two wells over a 15 - month period ( fig5 a ). for this problem , an exemplary optimizer may include three components : optimization solvers , forward simulation wrappers , and simulation data converters , each of which must be developed separately for reservoir property characterization , reservoir simulation , petrophysical property characterization , and 3d seismic simulation . the optimizer converges on a single “ best approximation ” result that simultaneously solves for permeability variations and flow rate changes , with error estimates for each . fig6 schematically shows the operation of the calm martingale 4d seisres controller system in this application . the controller system monitors the reservoir conditions , tracks actions taken , and uses machine learning continuously in real time , to generate a best action sequence . the best action sequence , for example , is constrained , for example , to ensure maximum profitability of the reservoir at a give time or cumulatively . fig5 b is an exemplary web - display of a producing reservoir &# 39 ; s drainage over time as simulated the calm martingale 4d seisres controller system . the figure shows a 3 × 3 grid of several amplitude mapping plots ( e . g ., observed , predicted , observed - observed difference , observed - predicted difference , predicted - predicted difference , and observed difference - predicted difference plots ) displayed in the web - based viewer . the calm martingale 4d seisres controller system provides a modern , real - time reservoir management system that is able to characterize multiple , sequential 4d seismic surveys ; seismic attribute volumes that vary with offset ; many repeated well logs of different types and vintages ; geostatistically - derived data volumes in both spatial and “ stratigraphic ” grids ; 3d fluid saturation volumes and fluid - flow maps ; fluid - interface monitors , multiple horizons and fault surfaces , and other data types that are at present only on the r & amp ; d drawing board ( fig7 ). fig7 shows the movement over time of oil toward two wells plotted on a reservoir horizon . the four snapshots ( a )-( d ) of drainage patterns were made by the 4d seisres system from interpolation using the reservoir simulator calibrated by 3d seismic surveys made at ( a ) and ( d ) 15 months apart . fig8 shows exemplary output of the calm martingale 4d system optimizer when permeability is varied from node to node of the reservoir multimesh to match flow rates of oil and gas by minimizing errors in predicted vs . observed production histories of the well and changes in 4d seismic amplitude over the 15 - month period . the calm martingale 4d seisres controller system takes a global view of all these different types of data , which are spatially registered with respect to one another in the local real - world coordinate system and rendered in a variety of modes so that the interrelationships can be perceived . this global view is a key to martingale control of an e - field , so that calm martingale 4d seisres controller system will deliver profitability that is “ always - in - the - money .” in accordance with the present disclosure , software ( i . e ., instructions ) for implementing the aforementioned controllers and systems can be provided on computer - readable media . it will be appreciated that each of the steps ( described above in accordance with this invention ), and any combination of these steps , can be implemented by computer program instructions . these computer program instructions can be loaded onto a computer or other programmable apparatus to produce a machine , such that the instructions , which execute on the computer or other programmable apparatus create means for implementing the functions of the aforementioned controllers and systems . these computer program instructions can also be stored in a computer - readable memory that can direct a computer or other programmable apparatus to function in a particular manner , such that the instructions stored in the computer - readable memory produce an article of manufacture including instruction means which implement the functions of the aforementioned controllers and systems . the computer program instructions can also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions of the aforementioned innervated stochastic controllers and systems . it will also be understood that the computer - readable media on which instructions for implementing the aforementioned controllers and systems are be provided , include without limitation , firmware , microcontrollers , microprocessors , integrated circuits , asics , and other available media . it will be understood that the foregoing is only illustrative of the principles of the invention , and that various modifications can be made by those skilled in the art , without departing from the scope and spirit of the invention , which is limited only by the claims that follow .