An Innovative Workflow for the Dynamic Modeling and History Matching of a Mature Complex Carbonate Oil Field in the Middle East

2014 ◽  
Author(s):  
Caroline Tomio ◽  
Jeremie Fernagu ◽  
Luther Thomas Sullivan ◽  
Abdulaziz Rashid Al Naimi
2021 ◽  
Author(s):  
Mathias Bayerl ◽  
Pascale Neff ◽  
Torsten Clemens ◽  
Martin Sieberer ◽  
Barbara Stummer ◽  
...  

Abstract Field re-development planning for tertiary recovery projects in mature fields traditionally involves a comprehensive subsurface evaluation circle, including static/dynamic modeling, scenario assessment and candidate selection based on economic models. The aforementioned sequential approach is time-consuming and includes the risk of delaying project maturation. This work introduces a novel approach which integrates subsurface geological and dynamic modeling as well as economics and uses machine learning augmented uncertainty workflows to achieve project acceleration. In the elaborated enhanced oil recovery (EOR) evaluation process, a machine learning assisted approach is used in order to narrow geological and dynamic parameter ranges both for model initialization and subsequent history matching. The resulting posterior parameter distributions are used to create the input models for scenario evaluation under uncertainty. This scenario screening comprises not only an investigation of qualified EOR roll-out areas, but also includes detailed engineering such as well spacing optimization and pattern generation. Eventually, a fully stochastic economic evaluation approach is performed in order to rank and select scenarios for EOR implementation. The presented workflow has been applied successfully for a mature oil field in Central/Eastern Europe with 60+ years of production history. It is shown that by using a fully stochastic approach, integrating subsurface engineering and economic evaluation, a considerable acceleration of up to 75% in project maturation time is achieved. Moreover, the applied workflow stands out due to its flexibility and adaptability based on changes in the project scope. In the course of this case study, a sector roll-out of chemical EOR is elaborated, including a proposal for 27 new well candidates and 17 well conversions, resulting in an incremental oil production of 4.7MM bbl. The key findings were: A workflow is introduced that delivers a fully stochastic economic evaluation while honoring the input and measured data.The delivered scenarios are conditioned to the geological information and the production history in a Bayesian Framework to ensure full consistency of the selected subsurface model advanced to forecasting.The applied process results in substantial time reduction for an EOR re-development project evaluation cycle.


2021 ◽  
Author(s):  
Arjan Matheus Kamp ◽  
Amna Khalid Alhosani ◽  
David Dong II Kim ◽  
Sophie Verdière ◽  
Hamdy Helmy Mohamed

Abstract As part of a reservoir modelling study for an onshore oil field in the Middle East, our study implemented a workflow with the objective to evaluate the impact of uncertainty on the long-term development scenario. The presence of several geological uncertainties characterized the field: many faults with uncertainty in juxtaposition and conductivity, lateral distribution of permeability in high permeability layers, and uncertainty on the rock typing. A deterministic geological model was available. There were also many dynamic uncertainties. The workflow started with an identification of uncertain variables, both from the static and the dynamic point of view, through an integrated team approach supported by a previous reservoir synthesis (Major Field Review). Subsequently, a screening analysis allowed identifying the relative impact of uncertain variables. After selecting the uncertainties with the largest impact on recovery, use of an experimental design methodology with a space-filling design resulted in alternative history matches. Statistical analysis of forecasts yielded probability density functions and low and high estimates of ultimate recovery. Forty-five uncertain variables, including both static and dynamic uncertainties, characterized the production profiles. Screening allowed reducing these to 11 main uncertain variables. A Wootton, Sergent, Phan-Tan-Luu (WSP) space-filling design yielded 162 simulation runs. Only five out of these corresponded to acceptable history matches. This number being statistically insignificant, a reexamination of the uncertainty ranges followed by a narrowing, allowed obtaining 45 history matches (out of 198 runs). The obtained spread in the cumulative oil production was narrow, with a slightly skewed distribution around the base case (closer to P90 than to P10). The study resulted in an estimation of final uncertainty in reserves that is smaller than the typical uncertainty found in post-mortem analysis of oil field development projects. Other reservoir studies in the company and in literature, employing a similar workflow, yielded outcomes with a similar bias. To tackle this issue, as a way forward we suggest history matching of multiple geological scenarios, either with multiple deterministic cases (min, base, max) or with an ensemble history matching loop including structural model generation, in-filling, and dynamic parameter uncertainty.


2021 ◽  
pp. 014459872199465
Author(s):  
Yuhui Zhou ◽  
Sheng Lei ◽  
Xuebiao Du ◽  
Shichang Ju ◽  
Wei Li

Carbonate reservoirs are highly heterogeneous. During waterflooding stage, the channeling phenomenon of displacing fluid in high-permeability layers easily leads to early water breakthrough and high water-cut with low recovery rate. To quantitatively characterize the inter-well connectivity parameters (including conductivity and connected volume), we developed an inter-well connectivity model based on the principle of inter-well connectivity and the geological data and development performance of carbonate reservoirs. Thus, the planar water injection allocation factors and water injection utilization rate of different layers can be obtained. In addition, when the proposed model is integrated with automatic history matching method and production optimization algorithm, the real-time oil and water production can be optimized and predicted. Field application demonstrates that adjusting injection parameters based on the model outputs results in a 1.5% increase in annual oil production, which offers significant guidance for the efficient development of similar oil reservoirs. In this study, the connectivity method was applied to multi-layer real reservoirs for the first time, and the injection and production volume of injection-production wells were repeatedly updated based on multiple iterations of water injection efficiency. The correctness of the method was verified by conceptual calculations and then applied to real reservoirs. So that the oil field can increase production in a short time, and has good application value.


2006 ◽  
Author(s):  
Shahab D. Mohaghegh ◽  
Hafez H. Hafez ◽  
Razi Gaskari ◽  
Masoud Haajizadeh ◽  
Maher Kenawy

2014 ◽  
Author(s):  
Hector Aguilar ◽  
Aref Almarzooqi ◽  
Tarek Mohamed El Sonbaty ◽  
Leigber Villarreal

2016 ◽  
Author(s):  
Mohammad Yunus Khan ◽  
Anupam Tiwari ◽  
Shuichiro Ikeda ◽  
Fahad I. Syed ◽  
Alunood K. Al Sowaidi ◽  
...  

2021 ◽  
Author(s):  
Ali Reham Al-Jabri ◽  
Rouhollah Farajzadeh ◽  
Abdullah Alkindi ◽  
Rifaat Al-Mjeni ◽  
David Rousseau ◽  
...  

Abstract Heavy oil reservoirs remain challenging for surfactant-based EOR. In particular, selecting fine-tuned and cost effective chemical formulations requires extensive laboratory work and a solid methodology. This paper reports a laboratory feasibility study, aiming at designing a surfactant-polymer pilot for a heavy oil field with an oil viscosity of ~500cP in the South of Sultanate of Oman, where polymer flooding has already been successfully trialed. A major driver was to design a simple chemical EOR method, to minimize the risk of operational issues (e.g. scaling) and ensure smooth logistics on the field. To that end, a dedicated alkaline-free and solvent-free surfactant polymer (SP) formulation has been designed, with its sole three components, polymer, surfactant and co-surfactant, being readily available industrial chemicals. This part of the work has been reported in a previous paper. A comprehensive set of oil recovery coreflood tests has then been carried out with two objectives: validate the intrinsic performances of the SP formulation in terms of residual oil mobilization and establish an optimal injection strategy to maximize oil recovery with minimal surfactant dosage. The 10 coreflood tests performed involved: Bentheimer sandstone, for baseline assessments on large plugs with minimized experimental uncertainties; homogeneous artificial sand and clays granular packs built to have representative mineralogical composition, for tuning of the injection parameters; native reservoir rock plugs, unstacked in order to avoid any bias, to validate the injection strategy in fully representative conditions. All surfactant injections were performed after long polymer injections, to mimic the operational conditions in the field. Under injection of "infinite" slugs of the SP formulation, all tests have led to tertiary recoveries of more than 88% of the remaining oil after waterflood with final oil saturations of less than 5%. When short slugs of SP formulation were injected, tertiary recoveries were larger than 70% ROIP with final oil saturations less than 10%. The final optimized test on a reservoir rock plug, which was selected after an extensive review of the petrophysical and mineralogical properties of the available reservoir cores, led to a tertiary recovery of 90% ROIP with a final oil saturation of 2%, after injection of 0.35 PV of SP formulation at 6 g/L total surfactant concentration, with surfactant losses of 0.14 mg-surfactant/g(rock). Further optimization will allow accelerating oil bank arrival and reducing the large PV of chase polymer needed to mobilize the liberated oil. An additional part of the work consisted in generating the parameters needed for reservoir scale simulation. This required dedicated laboratory assays and history matching simulations of which the results are presented and discussed. These outcomes validate, at lab scale, the feasibility of a surfactant polymer process for the heavy oil field investigated. As there has been no published field test of SP injection in heavy oil, this work may also open the way to a new range of field applications.


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