FIELD OF THE INVENTION

The invention relates to a method and a system for managing performance of a subterranean fluid reservoir penetrated by a wellbore based on predicted parameters characterizing a pore space of the reservoir formation.

BACKGROUND OF THE INVENTION

Knowledge of pore size distribution, pore wettability as a function of pore size and pore shape factors (aspect ratio, tortuosity etc.) is crucial to identify most appropriate techniques for optimizing hydrocarbon production and recovery (G.G.M. Villazon, R.F. Sigal, F. Civan, D. Devegowda "Parametric Investigation of Shale Gas Production Considering Nano-Scale Pore Size Distribution, Formation Factor, and Non-Darcy Flow Mechanisms" SPE-147438-MS, SPE Annual Technical Conference and Exhibition, 30 October-2 November, Denver, Colorado, USA, 2011 ; L. Jin, H. Pu, Y. Wang, Y. Li "The Consideration of Pore Size Distribution in Organic-Rich Unconventional Formations May Increase Oil Production and Reserve by 25%, Eagle Ford Case Study" SPE-178507-MS Unconventional Resources Technology Conference, 20-22 July, San Antonio, Texas, USA, 2015) . Another piece of data, which can be utilized for the same purposes, is information about fluid distribution between pores in terms of composition and phase conditions (free, adsorbed, condensed). Ability to obtain these characteristics of the reservoir without intervention of the fluid production is of high demand. One of the tools for bringing out an essential information for enabling an optimal way of reservoir development ensuring extraction of maximal amount of oil producible hydrocarbon are rapidly developing technologies based on Digital Rock studies (i.e. US20160063150). However, Digital Rock capabilities are solemnly based on knowledge of geometrical details of pore space and physico-chemical properties of a rock matrix. These properties could be acquired with some of the high resolution imaging techniques when core samples are available (i.e. US20150262417). Unfortunately, in many cases, the core samples are either not available or not representative to the reservoir. Therefore, even when the lab data are available one needs to find a way to reconstruct a relevant digital representation of in-situ rock. The current disclosure provides an alternative approach for getting the geometry and physico-chemical properties of shale and tight rocks pore space from abundantly available production data.

The majority of the published data related to the problem of production data analysis demonstrates importance of the pore size distribution (PSD) for the correct evaluation of the formation properties and reservoir productivity. Nevertheless most of these papers consider PSD as available from analysis of the rock samples (A. Sanaei, A. Jamili, J. Callard, A. Mathur "Production Modeling in the Eagle Ford Gas Condensate Window: Integrating New Relationships between Core Permeability, Pore Size, and Confined PVT Properties" SPE- 169493 -MS, SPE Western North American and Rocky Mountain Joint Meeting, 17-18 April, Denver, Colorado, 2014). Just a few authors utilize analysis based on application of wireline tools. In these cases an analysis of porosity distribution from the combination of the logs (C. Longis, S. Vignau, J.H. White "NMR and Capture Spectroscopy help Resolve Producibility and Fluid Distribution in the North Alwyn Triassic" SPE- 96609-MS, SPE Annual Technical Conference and Exhibition, 9-12 October, Dallas, Texas, 2005) or an analysis of the pressure build-up dynamics observed by wireline formation testers (L.L. Raymer, P.M. Freeman "In-Situ Determination Of Capillary Pressure, Pore Throat Size And Distribution, And Permeability From Wireline Data" SP WLA- 1984-CCC, SPWLA 25th Annual Logging Symposium, 10-13 June, New Orleans, Louisiana, 1984) is considered. These approaches have the following natural drawbacks.

Analysis of the rock samples give information on the rock that can be altered due to depressurization, change of the temperatures and contact with drilling fluids. Rock sampling is usually performed at the drilling stage and therefore do not reflect changes of the formation properties during production period. Furthermore in heterogeneous reservoir rock samples could be not representative for the most area of the reservoir.

Studies based on analysis of the data from the wireline tools provides information on the rock at in-situ conditions, but radius of the investigation of the wireline tool is still limited so results of analysis are representative only for small part of the productive layer and do not give information of the far zone of the reservoir. Additional drawback associated with application of the wireline tools is necessity of the well shut-in for the intervention and related technical problems / financial losses.

SUMMARY

The disclosed method for managing a fluid reservoir performance comprises storing a reservoir data comprising dependencies of a composition of a fluid produced from a reservoir on time or on a reservoir pressure or both and at least one parameter characterizing a pore space of the reservoir formation in a training database in a memory storage, the reservoir data obtained from a plurality of fluid reservoirs, and performing, by a cognitive system, an analysis of said reservoir data, wherein said analysis produces relationships between said dependencies of a composition of a fluid produced from a reservoir on time or on a reservoir pressure or both and said parameters characterizing a pore space of the reservoir formation. Then, the method includes inputting, into the cognitive system, dependencies of a composition of a fluid produced from a new reservoir on time or on a reservoir pressure or both and predicting, by the cognitive system, at least one parameter characterizing a pore space of the new reservoir formation based on the produced relationships. The method also includes planning at least one strategy of production or recovery optimization using the predicted parameters characterizing the pore space, and implementing the planned strategies using appropriate well completions, surface and subsurface equipment for fluid production monitoring and control.

According to one embodiment of the invention, a wellhead pressure can be used as the reservoir pressure; according to another embodiment, a bottomhole pressure can be used as the reservoir pressure.

The produced fluid can be a mixture of hydrocarbons.

BRIEF DESCRIPTION OF DRAWINGS

Fig. 1 shows a flowchart in accordance with one or more embodiments of the disclosure;

Fig. 2 illustrates a computing system in accordance with one or more embodiments;

Fig. 3 illustrates an example of two different pore systems; Fig. 4 illustrates produced fluid composition curves for the two pore systems shown on Fig.3.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying figures.

The method utilizes surface (wellhead) or downhole or laboratory measurements of composition of produced reservoir fluid versus time and/or reservoir pressure and predicts features of porous structure of reservoir formation. Dependence between the determined composition of a produced fluid as a function of time and/or pressure and parameters characterizing a pore space of the reservoir formation can be obtained either from the real sets of compositional measurements in various wells and analysis of the core samples from corresponding wells or by numerical modeling of fluid storage and transport in different porous structures. The porous structures used in numerical simulations can reproduce the key properties (pore size distribution, aspect ratio distribution, tortuosity, wettability etc.) of large variety of reservoir formations.

Fig. 1 shows a flowchart in accordance with one or more embodiments.

The disclosed method comprises storing by a computing system a training database in a memory storage a reservoir data obtained from a plurality of subterranean fluid reservoirs and comprising dependencies of a composition of a fluid produced from a reservoir on time or on a reservoir pressure or both and at least one parameter characterizing a pore space of the reservoir formation (Block 1).

In Block 2, an analysis of said reservoir data stored in the training database is performed by a cognitive system of the computing system, wherein said analysis produces relationships between said dependencies of a composition of a fluid produced from a reservoir on time or on a reservoir pressure or both and said parameters characterizing a pore space of the reservoir formation.

Said relationship can be obtained either from the real sets of compositional measurements in various wells and analysis of core samples from corresponding wells or by numerical modeling of fluid storage and transport in different porous structures. The porous structures used in numerical simulations can reproduce the key properties (pore size distribution, aspect ratio distribution, tortuosity, wettability etc.) of large variety of reservoir formations.

The cognitive system performs the data processing and analytics and can have a form of hardware with the pre-installed software or a software only distributed for installation on commonly used hardware with the installed commonly used operation systems. The cognitive system analyses the available information and establishes links and relations between the input dependencies of a composition of a fluid produced from a reservoir on time or on a reservoir pressure or both and the output parameters characterizing a pore space of the reservoir formation (e.g. pore size distribution, an aspect ratio distribution, a tortuosity, an inter-class connectivity, a total surface area, a specific surface area, a specific surface area distribution over pore classes, a pore wettability as a function of pore size).

In Block 3, new dependencies of a composition of a fluid produced from a new reservoir on time or on a reservoir pressure or both are submitted by a user into the cognitive system of the computing system via an interface.

In Block 4, the comparison of the parameters of the new reservoir (the new dependencies of the composition of the fluid produced from the new reservoir on time or on the reservoir pressure or both) with the same parameters obtained from a plurality of fluid reservoirs is performed by the cognitive system using data analytics techniques such as a machine learning and parameters characterizing a pore space of the new reservoir formation based on the produced relationships are predicted.

For the given input parameters of the new reservoir provided the cognitive system of the computing system predicts the output parameters - parameters characterizing a pore space of said new reservoir formation.

Workflow may also include planning at least one strategy of production or recovery optimization using the predicted parameters characterizing the pore space (Block 5), and implementing the planned strategies using appropriate well completions, surface and subsurface equipment for fluid production monitoring and control. (Block 6).

Examples of utilization of the predicted parameters characterizing the pore space for production strategy planning can be found in (M. Shoaib "Field Development Planning - A Key Towards Success in Rich Gas Condensate Reservoirs" SPE-178738-STU, SPE Annual Technical Conference and Exhibition, 28-30 September, Houston, Texas, USA, 2015 or A. Filippov, X. Jia, T. McNealy, V. Khoriakov "Fast Economic Analysis and Optimization of Fracture-Stimulated Wells in Condensate Reservoirs" SPE-179957-MS, SPE/IAEE Hydrocarbon Economics and Evaluation Symposium, 17-18 May, Houston, Texas, USA, 2016). Here predicted parameters characterizing the pore space are affecting key elements of the production models used for the planning such as, for example, phase behavior of the reservoir fluid, reservoir permeability and fluid phase permeabilities, and therefore should be known for adequate planning.

The computing system may be of virtually any type regardless of the platform being used. For example, the computing system may be one or more mobile devices (e.g., laptop computer, smartphone, smartwatch, personal digital assistant, tablet computer, or other mobile device), desktop computers, servers, blades in a server chassis, or any other type of computing device or devices that includes at least the minimum processing power, memory, and input and output device(s) to perform one or more embodiments of the invention.

Figure 2 shows an example of the computing system in accordance with some embodiments. The computing system may include a cognitive system 8 comprising a processor to perform the data analytics and generation of predictions, a memory storage 9 and a user interface 7.

The cognitive system 8 simulates the process of human thought using a numerical model. Cognitive systems use data mining, machine learning, pattern recognition and language processing techniques to perform the analysis of data (see Smart Machines: IBM's Watson and the Era of Cognitive Computing by John E. Kelly III, Columbia Business School Publishing, 160 p., 2013 for more details on the cognitive systems). These features enable the cognitive systems to efficiently perform the analytics on the data available in the petroleum industry and provide data driven predictions for the new reservoirs.

The computing system comprises the memory storage 9 (e.g., random access memory (RAM), cache memory, flash memory, etc.), one or more storage device(s) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory stick, etc.), and numerous other elements and functionalities.

Software instructions in the form of computer readable program code to perform one or more embodiments may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a CD, DVD, storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium. Specifically, the software instructions may correspond to computer readable program code that when executed by a processor(s), is configured to perform one or more embodiments of the method.

The computing system also comprises a user interface 7. The conventional user interface provides a means for one or more users to provide information to the system and retrieve information therefrom. Illustratively, the interface can be Windows-based graphical user interface (GUI) including a keyboard, a mouse and a display.

Further, one or more elements of the aforementioned computing system may be located at a remote location and connected to the other elements over a network. Further, embodiments may be implemented on a distributed system having multiple nodes, where each portion of an embodiment may be located on a different node within the distributed system. In one or more embodiments, the node corresponds to a distinct computing device. Alternatively, the node may correspond to a computer processor with associated physical memory or to a computer processor or micro- core of a computer processor with shared memory and/or resources.

Dependence of production dynamics of pore structure is illustrated in the following numerical simulation. Let us consider a system of slit nano-pores of different width staying in contact with a large matrix pore (borehole) (Fig.3a). At the initial moment the system is filled with mixture of methane and hexane with 6:1 composition ratio. Due to redistribution between tight pores and borehole volume the initial methane ihexane ratio of the produced fluid is about 7 (see Fig. 4). During the production process the composition of produced fluid changes with the reservoir pressure. The shape of the curve reflects the system properties. For similar system of cylindrical nano-pores (Fig.3b) the variation of produced fluid composition vs pressure is different (see Fig. 4). Thus, it is possible to link the shape of the produced fluid composition curve with particular pore space. The predictability of the approach increases with increase of the number of components in produced fluid. Fortunately, in real cases this number is huge, which favors the suggested approach.

The main advantages of the suggested method and system are: possibility to apply the proposed method at any stage of production; non-locality of estimated properties (i.e. properties are estimated for whole volume of the reservoir which contributes to the production from particular well, rather than based on small core sample collected); results are provided in situ; fluid storage distribution is estimated in terms of composition and phase state, and recovery mechanism and their evolution in time.

While the above has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments can be devised which do not depart from the scope as disclosed herein. Accordingly, the scope should be limited by the attached claims.