Integrating Pressure Data from Formation Tester Tool and DST to Characterize
Deepwater Fields, East Kalimantan, Indonesia

MacArthur, J.B.; Vo, D.T.; Palar, Steve; Terry, Albert; Brown, Trevor; Hariyadi, .; May, R.J.

doi:10.2523/59404-ms

Cited by 6 publications

(3 citation statements)

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“…For example, because pressure tests provide very little information on the influence of vertical variations in properties unless special tests are designed, we usually accept a very low resolution on this scale, and this assumption may result in a significant underestimate of the value (size) of the resource [ Raghavan et al , 2002; Raghavan and Kuchuck , 2004]. The difficulties involved in describing responses in reservoirs with complex geology by analytical models are given by MacArthur et al [2001].…”

Section: Discussionmentioning

confidence: 99%

A review of applications to constrain pumping test responses to improve on geological description and uncertainty

Raghavan

2004

Reviews of Geophysics

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[1] This review examines the single-phase flow of fluids to wells in heterogeneous porous media and explores procedures to evaluate pumping test or pressure-response curves. This paper examines how these curves may be used to improve descriptions of reservoir properties obtained from geology, geophysics, core analysis, outcrop measurements, and rock physics. We begin our discussion with a summary of the classical attempts to handle the issue of heterogeneity in well test analysis. We then review more recent advances concerning the evaluation of conductivity or permeability in terms of statistical variables and touch on perturbation techniques. Our current view to address the issue of heterogeneity by pumping tests may be simply summarized as follows. We assume a three-dimensional array (ordered set) of values for the properties of the porous medium as a function of the coordinates that is obtained as a result of measurements and interpretations. We presume that this array of values contains all relevant information available from prior geological and geophysical interpretations, core and outcrop measurements, and rock physics. These arrays consist of several million values of properties, and the information available is usually on a very fine scale (often <0.5 m in the vertical direction); for convenience, we refer to these as cell values. The properties are assumed to be constant over the volume of each of these cells; that is, the support volume is the cell volume, and the cell volumes define the geologic scale. In this view it is implicit that small-scale permeability affects the movement of fluids. Although more information than porosity is available, we refer to this system as a ''porosity cube.'' Because it is not economically possible to carry out computations on a finescale model with modern resources on a routine basis, we discuss matters relating to the aggregation and scale-up of the fine-scale model from the perspective of testing and show that specific details need to be addressed. The focus is on single-phase flow. Addressing the issue of scale-up also permits us to comment on the application of the classical or analytical solutions to heterogeneous systems. The final part of the discussion outlines the inversion process and the adjustment of cell values to match observed performance. Because the computational scale and the scale of the porosity cube are different, we recommend that the inversion process incorporate adjustments at the fine scale. In this view the scale-up process becomes a part of the inversion algorithm.

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Section: Discussionmentioning

confidence: 99%

A review of applications to constrain pumping test responses to improve on geological description and uncertainty

Raghavan

2004

Reviews of Geophysics

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“…This very important parameter is measured and reported through direct measurement of PP using the Modular Formation Dynamics Tester (MDT) (Carnegie et al, 2002;Baouche et al, 2020). Another tool that measures formation pressure is the Repeat Formation Tester (RFT), which is a routine tool in the petroleum industry (MacArthur et al, 2000;Matar et al, 2019;Jafarizadeh et al, 2022). But one of the reasons that has made the use of these tools problematic is that they are expensive and time-consuming to drive (Ma et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Application of GMDH model to predict pore pressure

et al. 2023

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Pore pressure (PP) is one of the essential and very critical parameters in the oil and gas industry, especially in reservoir engineering, exploitation, and production. Forecasting this valuable parameter can prevent huge costs incurred by the oil and gas industry. This research aims to develop a algorithm to better predict PP in subsurface -formations. Based on this, information from three wells (F1, F2, and F3) representing one of the Middle East oil fields was used in this research. The input variables used in this research include; laterolog (LLS), photoelectric index (PEF), compressional wave velocity (Vp), porosity (NPHI), gamma ray (spectral) (SGR), density (RHOB), gamma ray (corrected) (CGR), shear wave velocity (Vs), caliper (CALI), resistivity (ILD), and sonic transit time (DT). Based on the results presented in the heat map (Spearman’s correlation), it can be concluded that the pairs of parameters RHOB-PEF, CGR-SGR, RHOB-CALL, DT-PEF, PP-RHOB, Vs-RHOB, ILD-LLS, DT-CGR, and DT-NPHI are connected. In this research the GS-GMDH methods is used for modeling which is based on the Group method of data handling (GMDH). The results of this research show that this algorithm has an average error of RMSE = 1.88 Psi and R2 = 0.9997, indicating its high-performance accuracy. The difference between this method and the conventional GMDH method is that it can use three or more variables instead of two, which can improve prediction accuracy. Furthermore, by using the input of each neuron layer, the proposed model can communicate with other adjacent and non-adjacent layers to solve complex problems in the simplest possible way.

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“…However, this is not practical or cost effective and one must collect data judiciously such that well/reservoir performance can be accurately determined 1 . This is an especially important consideration in the deep water environment where the cost of DST's in every well will not be justified and using lower cost MDT, CMR and/or core samples in more wells will have to suffice.…”

Section: Introductionmentioning

confidence: 99%

So What is the Reservoir Permeability?

Haddad

Cribbs²,

Sagar

et al. 2000

SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIn many reservoirs, permeability values are often available from one or as many as four sources. These permeability sources include core analysis, nuclear magnetic resonance log (NMR), wireline formation tester, and well testing (DST). However, permeability values from these sources often differ significantly. In many cases, this creates confusion as to which value of permeability should be used to best represent reservoir performance and well productivity.In this paper, permeability values, evaluation methods, scales and applicability of all measurements are discussed and verified with field data.Factors influencing each measurement technique are also discussed.Available permeability data from the various sources in a deep water Gulf of Mexico sandstone reservoir are considered to demonstrate how they may be used to build a model of well/reservoir performance. An accurate knowledge of permeability is critical to forecasting rates. Production facilities may be over-or under-designed if rates are inaccurately predicted, excessive capital expenditure for an over-designed facility or the inability to deliver at high rates for an under-designed facility can cost an operator and its partners dearly.

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Integrating Pressure Data from Formation Tester Tool and DST to Characterize Deepwater Fields, East Kalimantan, Indonesia

Cited by 6 publications

References 1 publication

A review of applications to constrain pumping test responses to improve on geological description and uncertainty

A review of applications to constrain pumping test responses to improve on geological description and uncertainty

Application of GMDH model to predict pore pressure

So What is the Reservoir Permeability?

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