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Given sufficient performance and other data, material balance (MB) is a common method of determining the hydrocarbons initially in-place (HCIIP) in a reservoir. The application of this method requires, as a minimum, historic cumulative production (including injection) and average reservoir pressure. However, determination of historic average reservoir pressures would require shut-in of wells, hence production deferments. As an improvement to the classical MB, the dynamic material balance (DMB) method was developed by Mattar and Anderson (2005). Unlike the MB method, direct measurements of average reservoir pressure are not critical to DMB. In its basic form, the implementation of DMB requires historic production rates, flowing bottomhole pressures and cumulative production, thereby eliminating associated deferments. Although DMB has performed satisfactorily in some applications, its overall robustness remains to be fully explored. This paper conducts rigorous sensitivity checks on selected DMB models. Based on insights gained, their relative strengths and weaknesses are highlighted. To keep the problem tractable, detailed simulations are performed on different three-dimensional (3D) multiphase homogenous reservoir models of known HCIIP. Different cases are simulated, generating relevant performance datasets to evaluate DMB. The parametric tests conducted on this undersaturated compressible oil reservoir include (i) constant vs. variable production rates; (ii) rate hysteresis; (iii) vertical vs. horizontal well; (iv) single vs. multiple wells; (v) healthy vs. damaged well; and (vi) variable skin factors, with hysteresis. Within the parameter space examined, simulation results show that DMB performance (e.g. HCIIP) is sensitive to some of the parameters and subsurface realisations investigated. Against this background, some improvements and guidelines are proposed to enhance the capability and performance of DMB as a technique for reservoir surveillance.
Given sufficient performance and other data, material balance (MB) is a common method of determining the hydrocarbons initially in-place (HCIIP) in a reservoir. The application of this method requires, as a minimum, historic cumulative production (including injection) and average reservoir pressure. However, determination of historic average reservoir pressures would require shut-in of wells, hence production deferments. As an improvement to the classical MB, the dynamic material balance (DMB) method was developed by Mattar and Anderson (2005). Unlike the MB method, direct measurements of average reservoir pressure are not critical to DMB. In its basic form, the implementation of DMB requires historic production rates, flowing bottomhole pressures and cumulative production, thereby eliminating associated deferments. Although DMB has performed satisfactorily in some applications, its overall robustness remains to be fully explored. This paper conducts rigorous sensitivity checks on selected DMB models. Based on insights gained, their relative strengths and weaknesses are highlighted. To keep the problem tractable, detailed simulations are performed on different three-dimensional (3D) multiphase homogenous reservoir models of known HCIIP. Different cases are simulated, generating relevant performance datasets to evaluate DMB. The parametric tests conducted on this undersaturated compressible oil reservoir include (i) constant vs. variable production rates; (ii) rate hysteresis; (iii) vertical vs. horizontal well; (iv) single vs. multiple wells; (v) healthy vs. damaged well; and (vi) variable skin factors, with hysteresis. Within the parameter space examined, simulation results show that DMB performance (e.g. HCIIP) is sensitive to some of the parameters and subsurface realisations investigated. Against this background, some improvements and guidelines are proposed to enhance the capability and performance of DMB as a technique for reservoir surveillance.
One of the most important features that impacts the facility size and production rate commitment is the minimum connected volume which can be explored through pressure Build Up and any misinterpretation, costs the operators significantly. This paper aims to address the challenges in Late Time Response analysis of pressure Build up well tests that exploits the reservoir boundary. Knowing that the well test response at late time shows the fingerprint of multiple features such as aquifer, sand discontinuity, baffles, faults, boundaries, etc., it will be challenging to quantify the extends of the reservoir using convolved late response. Two main numerical models were built for oil and gas reservoirs in an elongated reservoir to study the lateral boundary features such as closed boundary and aquifer and understand the effect of these features on late time response to get a clearer response. Using two models, the impact of mobility contrast between the hydrocarbon, boundary distance, aquifer strength, boundary movement and structure complexity was simulated. The major finding of this paper is that a unit slope straight line can be seen in buildup test which doesn't fit any classical and analytical model for pressure build up as the expectation is to have negative slope and down-ward hump for build up response of closed boundary and aquifer. However, this can be explained through mobility reduction from hydrocarbon (oil/gas) to water. The simulation time (build up test duration) was extended to see the long-term impact theoretically and at the end the same expectation was confirmed by simulation. After radial flow, channel flow (half slope) and then unit slope (mobility reduction) and then at last the drop in the derivative response is observed. However, the drop as signature of aquifer or limited volume happened beyond practical test duration (> 1000 hours). The work highlights the true workflow to identify the true response from reservoir and have reliable reservoir characterization and shows the case studies that address the cost on 1) misinterpretation of late time response and 2) excluding the well test from analysis due to its complexity to highlight the value and criticality of the integrated analysis.
Production analytical approaches specially in presence of smart well completion equipped with several PDGs, if applied appropriately, can overcome many challenges in study of complex reservoirs with comingled production that numerical modelling alone is not capable of. The objective of this work was to firstly utilize automation and programming to compile the massive amount of PDG data in smart completion and offer a workflow to overcome the missing data and challenges for analysis. Secondly this work customs a blend of different analytical approaches such as PTA, RTA, Material Balance combined with Teager-Keiser Energy Method to understand the many unknowns and uncertainties of water injection effectiveness in one of the heavily faulted multi-stack reservoirs in East Malaysia. For a proper reservoir characterization, knowing of accurate rate is a must, yet an accurate allocation itself is a function of formation properties, mainly permeability and skin. With the help of scripting, data from PDG is compiled. Preprocessing has been done to identify the fluid type produced from each layer. A workflow has been introduced to tackle the back allocation issue with FCV variation while allowing the luxury of full dynamic characterization of multi-stack reservoirs producing from single string. While every analytical approach has its own limitations and strengths, this work showcased how to apply different methods depends on the situation to squeeze the maximum information whether is to understand the injector-benefiter pairing, fault sealing and compartmentalization, connected volume, or fine migration and loss of injectivity. Processing of PDG big data made it possible for a dynamic interpretation of whole production period rather than selective time span. This work made us able to answer some of the last longing question such as the cause for injectivity deterioration, proper reservoir characterization aligned with geological understanding, and proper allocation suggested by new workflow. PTA results have been validated along other analytical methods such as RTA and material balance to understand connected volume, fault transmissibility and infill well potential. Teager-Keiser showed extremely helpful when combined with previous methods to recognize benefiter-injector pairing before any lagging evidence such as GOR and pressure trend, water production and salinity test can confirm the connectivity. Apart from the workflow to interpret the PDG of smart well completion producing commingled from multi-stack reservoirs, the work has been benefited from digitalization using open-source programming. The idea of dynamic data interpretation, how to deal with missing production data to enhance the accuracy of PTA analysis, and cross-validation through different approaches to shed light on the unknown of reservoir complexities to revisit and optimize water injection scheme, are the other highlights of this study.
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