Targeting and Developing the Remaining Pay in an Ageing Field: the Ovhor Field Experience

Abstract Understanding the complexity in the distribution of hydrocarbon in a simple structure with flow baffles and connectivity issues is critical in targeting and developing the remaining pay in a mature asset. Subtle facies changes (heterogeneity) can have drastic impact on reservoir fluids movement, and this can be crucial to identifying sweet spots in mature fields. This study evaluated selected reservoirs in Ovhor Field, Niger Delta, Nigeria with the objective of optimising production from the field by targeting undeveloped oil reserves or bypassed pay and gaining an improved understanding of the selected reservoirs to increase the company's reserves limits. The task at the Ovhor field, is complicated by poor stratigraphic seismic resolution over the field. 3-D geological (Sedimentology and stratigraphy) interpretation, Quantitative interpretation results and proper understanding of production data have been used in recognizing flow baffles and undeveloped compartments in the field. The full field 3-D model was constructed in such a way as to capture heterogeneities and the various compartments in the field. This was crucial to aid the simulation of fluid flow in the field for proper history matching, future production, prediction and design of well trajectories to adequately target undeveloped oil in the field. Reservoir property models (Porosity, Permeability and Net-To-Gross) were constructed by biasing log interpreted properties to a defined environment of deposition model whose interpretation captured the heterogeneities expected in the studied reservoirs. At least, two scenarios were modelled for the studied reservoirs to capture the range of uncertainties. This integrated approach led to the identification of bypassed oil in some areas of the selected reservoirs and an improved understanding of the studied reservoirs. Dynamic simulation and production forecast on the 4 reservoirs gave an undeveloped reserve of about 3.82 MMstb from two (2) identified oil restoration activities. These activities included side-tracking and re-perforation of existing wells. New wells have been drilled to test the results of our studies and the results confirmed our findings.

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Integrated Approach to Reserve Estimation and Reservoir Simulation of ENI Offshore Field, in Niger Delta Nigeria

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2019/v38i630425 ◽

2019 ◽

pp. 1-14

Author(s):

J. O. Ayorinde ◽

O. O. Osinowo ◽

P. Nwankwo

Keyword(s):

Niger Delta ◽

History Matching ◽

Continuous Production ◽

Material Balance ◽

Integrated Approach ◽

Reserve Estimation ◽

Oil Reserves ◽

Future Production ◽

Stock Tank ◽

World Energy

Two producing reservoirs (H10 and E40) in Eni field Offshore Niger Delta were studied with intent to enhance their rate of recovery while mitigating water production. The material Balance software MBAL was used to estimate the Stock tank oil reserves and then compared to reserve estimates determined by both deterministic and stochastic techniques for improved validation. The MBAL model was also used to identify positions of fluid contacts and determine predominant drive mechanisms. These serve as guide in making informed decisions towards if and how best to economically produce remaining unproduced oil in place. Input parameters were average values derived from core and well logs analyses. History matching of historical data enabled forecasts of possible future production life and volume at multiple scenarios. Final outcomes show that after sixteen and forty five years of continuous production from the reservoirs studied (H10 and E40, respectively), remaining unproduced oil in place are still significant and can be economically produced by infill wells, which will in return increase the average production by nothing less than 33% of remaining oil in place, a substantial value bearing in mind the growing demand for oil, gas and other energy sources to lessen the apparently unquenchable world energy needs.

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IDENTIFICATION OF INHOMOGENEOUS LAYERS AND RESERVOIR LENSES IN TERRIGENOUS DEPOSITS

Neft i gaz ◽

10.37878/2708-0080/2021-1.02 ◽

2020 ◽

Vol 1 (121) ◽

pp. 25-39

Author(s):

S.P. NOVIKOVA ◽

◽

S.V. SIDOROV ◽

Z.M. RIZVANOVA ◽

I.Z. FARXUTDINIV ◽

...

Keyword(s):

Structural Factor ◽

Upper Devonian ◽

Residual Oil ◽

Reservoir Properties ◽

Systematic Analysis ◽

Oil Saturation ◽

Oil Reserves ◽

Frasnian Stage ◽

Net To Gross ◽

Bypassed Oil

The possibilities of localizing residual oil reserves in depleted deposits of Tatarstan oilfield analyze in the article. The object of the study is the Pashian deposits of the Frasnian stage, Upper Devonian of the Almetyevskaya area, Romashkino oilfield. Possibilities of bypassed oil searching are considered. Sedimentation and heterogeneity of strata in the terrigenous Devonian sediments within the study area are analyzed in the paper. The analysis was carried out on the basis of well logging data. A detailed correlation of strata has been carried out, and members have been identified according to the rhythm of the layers’ occurrence in the Pashian horizon. The distribution of reservoirs and seals was studied based on the results of the detailed correlation. The analysis is based on a systematic analysis of the research object, which made it possible to consider the problem from different sides. The analysis of structural surfaces, maps of total and oil-saturated thicknesses, porosity, oil saturation, net-to-gross content and dissection along the upper and lower Pashian deposits was carried out. The influence of the structural factor on the reservoir properties of the formations is analyzed. The analysis made it possible to assess the heterogeneity of reservoirs in area and section and to predict the bypassed oil reserves localization.

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Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis

Journal of Artificial Evolution and Applications ◽

10.1155/2008/263108 ◽

2008 ◽

Vol 2008 ◽

pp. 1-13 ◽

Cited By ~ 9

Author(s):

Tina Yu ◽

Dave Wilkinson ◽

Alexandre Castellini

Keyword(s):

Genetic Programming ◽

History Matching ◽

Flow Simulation ◽

Simulation Models ◽

Time Frame ◽

Reservoir Modeling ◽

Production Data ◽

Production Forecast ◽

Future Production

Reservoir modeling is a critical step in the planning and development of oil fields. Before a reservoir model can be accepted for forecasting future production, the model has to be updated with historical production data. This process is called history matching. History matching requires computer flow simulation, which is very time-consuming. As a result, only a small number of simulation runs are conducted and the history-matching results are normally unsatisfactory. This is particularly evident when the reservoir has a long production history and the quality of production data is poor. The inadequacy of the history-matching results frequently leads to high uncertainty of production forecasting. To enhance the quality of the history-matching results and improve the confidence of production forecasts, we introduce a methodology using genetic programming (GP) to construct proxies for reservoir simulators. Acting as surrogates for the computer simulators, the “cheap” GP proxies can evaluate a large number (millions) of reservoir models within a very short time frame. With such a large sampling size, the reservoir history-matching results are more informative and the production forecasts are more reliable than those based on a small number of simulation models. We have developed a workflow which incorporates the two GP proxies into the history matching and production forecast process. Additionally, we conducted a case study to demonstrate the effectiveness of this approach. The study has revealed useful reservoir information and delivered more reliable production forecasts. All of these were accomplished without introducing new computer simulation runs.

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Caveats and Pitfalls of Production Forecast Uncertainty Analysis Using Design of Experiments

10.2118/203919-ms ◽

2021 ◽

Author(s):

Boxiao Li ◽

Hemant Phale ◽

Yanfen Zhang ◽

Timothy Tokar ◽

Xian-Huan Wen

Keyword(s):

Design Of Experiments ◽

Uncertainty Analysis ◽

History Matching ◽

Petroleum Industry ◽

Production Forecast ◽

Forecast Uncertainty ◽

Field Development ◽

Development Alternatives ◽

Modeling Principles ◽

Made In

Abstract Design of Experiments (DoE) is one of the most commonly employed techniques in the petroleum industry for Assisted History Matching (AHM) and uncertainty analysis of reservoir production forecasts. Although conceptually straightforward, DoE is often misused by practitioners because many of its statistical and modeling principles are not carefully followed. Our earlier paper (Li et al. 2019) detailed the best practices in DoE-based AHM for brownfields. However, to our best knowledge, there is a lack of studies that summarize the common caveats and pitfalls in DoE-based production forecast uncertainty analysis for greenfields and history-matched brownfields. Our objective here is to summarize these caveats and pitfalls to help practitioners apply the correct principles for DoE-based production forecast uncertainty analysis. Over 60 common pitfalls in all stages of a DoE workflow are summarized. Special attention is paid to the following critical project transitions: (1) the transition from static earth modeling to dynamic reservoir simulation; (2) from AHM to production forecast; and (3) from analyzing subsurface uncertainties to analyzing field-development alternatives. Most pitfalls can be avoided by consistently following the statistical and modeling principles. Some pitfalls, however, can trap experienced engineers. For example, mistakes made in handling the three abovementioned transitions can yield strongly unreliable proxy and sensitivity analysis. For the representative examples we study, they can lead to having a proxy R2 of less than 0.2 versus larger than 0.9 if done correctly. Two improved experimental designs are created to resolve this challenge. Besides the technical pitfalls that are avoidable via robust statistical workflows, we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R2. Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios.

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Integrated Production Model as a Tool for Optimization the Development Strategy of the Sakhalin Oil and Gas Condensate Field

10.2118/208502-ms ◽

2021 ◽

Author(s):

Oleksandr Doroshenko ◽

Miljenko Cimic ◽

Nicholas Singh ◽

Yevhen Machuzhak

Keyword(s):

History Matching ◽

Effective Field ◽

Production Optimization ◽

Production Model ◽

Production Forecast ◽

Hydrocarbon Production ◽

Integrated Production ◽

Wellhead Pressure ◽

Field Development ◽

The Impact

Abstract A fully integrated production model (IPM) has been implemented in the Sakhalin field to optimize hydrocarbons production and carried out effective field development. To achieve our goal in optimizing production, a strategy has been accurately executed to align the surface facilities upgrade with the production forecast. The main challenges to achieving the goal, that we have faced were:All facilities were designed for early production stage in late 1980's, and as the asset outdated the pipeline sizes, routing and compression strategies needs review.Detecting, predicting and reducing liquid loading is required so that the operator can proactively control the hydrocarbon production process.No integrated asset model exists to date. The most significant engineering tasks were solved by creating models of reservoirs, wells and surface network facility, and after history matching and connecting all the elements of the model into a single environment, it has been used for the different production forecast scenarios, taking into account the impact of infrastructure bottlenecks on production of each well. This paper describes in detail methodology applied to calculate optimal well control, wellhead pressure, pressure at the inlet of the booster compressor, as well as for improving surface flowlines capacity. Using the model, we determined the compressor capacity required for the next more than ten years and assessed the impact of pipeline upgrades on oil gas and condensate production. Using optimization algorithms, a realistic scenario was set and used as a basis for maximizing hydrocarbon production. Integrated production model (IPM) and production optimization provided to us several development scenarios to achieve target production at the lowest cost by eliminating infrastructure constraints.

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Development of Bypassed Oil Reserves Using Behind Casing Resistivity Measurements

10.2172/901294 ◽

2004 ◽

Author(s):

Michael G. Conner

Keyword(s):

Resistivity Measurements ◽

Oil Reserves ◽

Bypassed Oil

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Numerical Comparison Between ES-MDA and Gradient-Based Optimization for History Matching of Reduced Reservoir Models

10.2118/203975-ms ◽

2021 ◽

Author(s):

M. A. Borregales Reverón ◽

H. H. Holm ◽

O. Møyner ◽

S. Krogstad ◽

K.-A. Lie

Keyword(s):

History Matching ◽

Automatic Differentiation ◽

Computational Cost ◽

Numerical Comparison ◽

Petroleum Reservoir ◽

History Match ◽

Reservoir Models ◽

Multiple Data ◽

Future Production ◽

Gradient Based

Abstract The Ensemble Smoother with Multiple Data Assimilation (ES-MDA) method has been popular for petroleum reservoir history matching. However, the increasing inclusion of automatic differentiation in reservoir models opens the possibility to history-match models using gradient-based optimization. Here, we discuss, study, and compare ES-MDA and a gradient-based optimization for history-matching waterflooding models. We apply these two methods to history match reduced GPSNet-type models. To study the methods, we use an implementation of ES-MDA and a gradient-based optimization in the open-source MATLAB Reservoir Simulation Toolbox (MRST), and compare the methods in terms of history-matching quality and computational efficiency. We show complementary advantages of both ES-MDA and gradient-based optimization. ES-MDA is suitable when an exact gradient is not available and provides a satisfactory forecast of future production that often envelops the reference history data. On the other hand, gradient-based optimization is efficient if the exact gradient is available, as it then requires a low number of model evaluations. If the exact gradient is not available, using an approximate gradient or ES-MDA are good alternatives and give equivalent results in terms of computational cost and quality predictions.

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An Integrated Approach for History Matching of Multiscale-Fracture Reservoirs

SPE Journal ◽

10.2118/195589-pa ◽

2019 ◽

Vol 24 (04) ◽

pp. 1508-1525

Author(s):

Mengbi Yao ◽

Haibin Chang ◽

Xiang Li ◽

Dongxiao Zhang

Keyword(s):

History Matching ◽

Large Scale ◽

Fractured Reservoirs ◽

Integrated Approach ◽

Small Scale ◽

Gaussian Field ◽

Limited Information ◽

Fractured Reservoir ◽

Discrete Fracture Model ◽

Single Scale

Summary Naturally or hydraulically fractured reservoirs usually contain fractures at various scales. Among these fractures, large-scale fractures might strongly affect fluid flow, making them essential for production behavior. Areas with densely populated small-scale fractures might also affect the flow capacity of the region and contribute to production. However, because of limited information, locating each small-scale fracture individually is impossible. The coexistence of different fracture scales also constitutes a great challenge for history matching. In this work, an integrated approach is proposed to inverse model multiscale fractures hierarchically using dynamic production data. In the proposed method, a hybrid of an embedded discrete fracture model (EDFM) and a dual-porosity/dual-permeability (DPDP) model is devised to parameterize multiscale fractures. The large-scale fractures are explicitly modeled by EDFM with Hough-transform-based parameterization to maintain their geometrical details. For the area with densely populated small-scale fractures, a truncated Gaussian field is applied to capture its spatial distribution, and then the DPDP model is used to model this fracture area. After the parameterization, an iterative history-matching method is used to inversely model the flow in a fractured reservoir. Several synthetic cases, including one case with single-scale fractures and three cases with multiscale fractures, are designed to test the performance of the proposed approach.

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