FIELD-STUDY OF INTEGRATED FORMATION EVALUATION IN THINLY LAMINATED RESERVOIRS

Transcription

1 FIELD-STUDY OF INTEGRATED FORMATION EVALUATION IN THINLY LAMINATED RESERVOIRS Kamlesh Saxena (Reliance Industries Ltd.) and Theodore Klimentos (Schlumberger) Copyright 2004, held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. This paper was prepared for presentation at the SPWLA 45 th Annual Logging Symposium held in Noordwijk, The Netherlands, June 6 9, ABSTRACT The major problem in studying thin-layered reservoirs is identification of net pays and reliable assessment of hydrocarbon- type and degree. The main difficulty arises from the low vertical resolution of standard resistivity tools. More specifically, in laminated formations, thinbeds of fine-grained sand, silt, and clay distributed within hydrocarbon-bearing formations significantly reduce the apparent resistivity. Such fine-grained thin-bedded layers can hold high volumes of irreducible water and thus produce water-free hydrocarbons; yet oil companies may not even attempt to complete such zones. For example, one offshore deepwater well off the east coast of India, was recently tested over two thinly laminated sand/shale zones and produced dry gas of approximately 34 Mscf/D. However, this gas production was not expected by the initial conventional normal resolution petrophysical evaluation, which was largely underestimating the hydrocarbon potential over these two tested zones. The problem becomes more acute in deepwater formation evaluation since exploration and development costs are much higher and thus it is essential that all hydrocarbon reserves should be accurately assessed. This paper is a case-study of a field-scale integrated thin-bed petrophysical evaluation and hydrocarbon reserve estimation in the field-a (offshore India). All the drilled wells in this field intersected thinly laminated shale-silt-sand sequences and blocky sands. It is shown that in this environment using conventional resistivity data to quantify the hydrocarbon reserves is difficult because the data are dominated by the conductivity of the shale laminae. A model has been developed to use two resistivity measurements (vertical and horizontal), in conjunction with other available logs (nuclear magnetic resonance and formation microresistivity) and core data, in order to better predict the potential hydrocarbon in place. The final results were compared with the ones derived by conventional petrophysical evaluation. Use of this technique in several gas-bearing, thinly bedded intervals has resulted in gas reserve estimate increases up to 700 percent of the conventionally computed reserves. These results are confirmed against a variety of independent data, including surface seismic interpretations, reservoir pressure profiles core analysis and well tests. INTRODUCTION The field-a wells intersected several gas-bearing blocky sands and a large amount of thinly-bedded and laminated sections, which contain significant hydrocarbon pay and thus contribute to the total reserves. Using conventional resistivity logs in this environment, quantification of the hydrocarbon reserve potential is difficult because the data is dominated by the conductivity of the adjacent shale laminae 1. Thus, the major problem in studying the field-a thin-layered reservoirs is identification of net pays and reliable assessment of hydrocarbon. The difficulty arises from the low vertical resolution of standard and high-resolution resistivity tools. More specifically, in these laminated formations, thin-beds of fine-grained sand, silt, and clay distributed within the hydrocarbon-bearing formations significantly reduce the apparent resistivity. Such fine-grained thin-bedded layers can hold high volumes of irreducible water and thus produce water-free hydrocarbons. For example, well X was recently tested over two thinly laminated sand/shale zones and produced dry gas of approximately 34 Mscf/D. However, this gas production was not SSS 1

2 expected by the initial conventional normal resolution petrophysical evaluation, which was largely underestimating the hydrocarbon potential over the two tested zones. The thin-bed evaluation problem becomes more serious in this deep-water field because deepwater exploration and development costs are much higher; therefore, it is vital that all gas reserves should be accurately estimated. Therefore, to better account for the laminated character of the thinly bedded field-a reservoirs and for the fact that, when the hydrocarbon is gas, it is capable, over geological times, to displace capillary-bound water in silts and to be produced, a thin-bed analysis field study was performed. The thin-bed petrophysical analysis methodology used, i.e., Resistivity Anisotropy 1,2,3,4,5, instead of identifying and revealing individual thin-beds in a unit of rock, it studies the resistivity anisotropy caused by the presence of thin-bedded sand-shale sequences; firstly, the horizontal and vertical resistivities are determined and then used in the computation of R-sand and R-shale. Next, a thin-bed petrophysical evaluation is performed using a bi-modal petrophysical model. In a unit of rock, the model accounts for hydrocarbon held in a clean sand (perhaps with some dispersed clay), as well as hydrocarbon possibly contained in shale-sand-silt laminae. The following sections first describe the objectives, workflow and the basic principles of the methodology used; then, the Resistivity Anisotropy thin-bed petrophysical evaluation results are presented; the final section includes the summary and conclusions THEORY-METHODOLOGY Resistivity Anisotropy Principles Thin beds are beds thinner than the vertical resolution of the logging devices, as shown in Fig. 1. Thin beds of clay, silt and fine-grained sand distributed within a hydrocarbon bearing sand, as shown in Fig.2, significantly reduce the apparent resistivity measured by an induction or lateralog tool. Thus, in this case the resistivity will be reading low despite the presence of hydrocarbons. Moreover, the fine-grained layers have high irreducible water and therefore the reservoir can produce oil or gas with z 1 ero water-cut. With the technological advances of resistivity tools, the thin bed definition improved from about 3 feet for a conventional dual-laterolog to about 1 ft with the recent introduction of advanced laterologs (HALS*, ARI* and HRLA*).Nevertheless, as demonstrated by core observations, as shown in Fig. 2, thin beds can go down to the millimetric scale. Similar observations were made during the petrography LamCount analysis performed on selected core samples from the field-a, as shown in Fig. 3. When thin laminae of sand and shale are intersected, the resistivity tool electrical current is shorted due to the high conductivity of the shale laminae. Consequently, the hydrocarbon-bearing sand layers, although being more resistive, may not be detected. For example, if reservoir layers with a resistivity in excess of 100 Ohm.m are sandwiched within 50% volume of shale laminae, with a shale resistivity of 1 Ohm.m, will result in an R H reading not exceeding 2 Ohm.m, as shown in Fig. 4. When producing the field-a thin-bedded reservoirs, the contributions of many laminae will be cumulated together. Therefore, it is the cumulative volumetrics and hydrocarbon that would control the field-a reservoir behavior in a macroscopic manner. Recent methodologies 1,3,4,5 can obtain formation resistivity measurements both perpendicular and parallel to the direction of the shale-sand layers, i.e., Rv and R H, respectively. The vertical resistivity measures the reservoir and shale-silt laminations in series; thus, it keeps its sensitivity to reservoir laminae, by reducing the low resistivity phenomenon observed in thinly-bedded formations. Using R H and R V as inputs in a bimodal sand-shale laminar model, as shown in Fig. 5, we can then solve for the two unknowns Rshale and Rsand. The formation resistivity anisotropy is obtained by acquiring a combination of array induction and array laterolog data in the same tool string. It is well established that the electrical current loops induced by conventional induction logging instruments are sensing only the horizontal *Mark of Schlumberger 2

3 direction in a vertical well. Therefore, the AIT resistivity arrays and/or LWD CDR*/ARC* resistivities can be used to describe the horizontal resistivity R H, as shown in Fig. 6. Moreover, the HRLA, High Resolution Laterolog Array tool, which consists of an array of five laterologs with incremental depth of investigation, is affected by both invasion and resistivity anisotropy; the shallowest the depth of investigation of the HRLA array, the stronger its sensitivity to vertical resistivity. Therefore, a joint inversion of AIT and HRLA data was performed using a 2D geometrical model of the formation, including invaded and non-invaded beds. The inversion process yielded the horizontal and vertical resistivities of the studied formation model. However, the inversion process does not have a unique solution and depends on the nature of the modeled bedding. The characterization of the thin beds is significantly improved when using a Formation Micro resistivity Imager log (FMI*). This refines the formation model due to the fact that it enhances the modeling of thin, tight resistive layers that can be precisely detected by FMI and thus accounted for by the inversion. Any input log, i.e., CMR* 2, VCL, GR, etc., along with an interpreter s defined threshold, can be used to define whether a bed is invaded or non-invaded. As shown by the sand-shale laminated model in Fig. 5, we can derive the following equations: the horizontal resistivity data Rh senses both the horizontal shale resistivity (RshaH) and the sand resistivity (Rsand) in parallel: (1/ Rh)=(fVsha / RshaH) + (fvsand / Rsand) (1) the vertical resistivity data Rv senses both the vertical shale resistivity (RshaV) and sand resistivity (Rsand) in series: Rv = (fvsha x RshaV) + (fvsand x Rsand) (2) the sum of the fractional volumes of the two constituents, i.e., sand and shale which are the only two components of the model, is equal to one: fvsand + fvsha = 1 (3) The above system of equations consists of 5 unknowns, Rsand, RshaH, RshaV, fvsha and *Mark of Schlumberger fvres for only 3 equations. To solve this system we assume that the shale micro-anisotropy coefficient lamda= RshaV / RshaH is constant throughout the evaluated interval and the RshaH value is kept free to vary. The micro-anisotropy coefficient is selected by the interpreter; this reduces the number of unknowns to 4. Moreover, one extra input is the fractional volume of the laminated shale, i.e., fvsha. This can be obtained by a preliminary conventional petrophysical evaluation, using all available log data, or simply from CMR 1 and FMI data. Water can then be computed via a bimodal approach used for resistivity evaluation 1, i.e., the formation is split into its two constituents, sand reservoir with structural shale, and shale/silt laminae. RESISTIVITY ANISOTROPY PROCESSING WORKFLOW The Resistivity Anisotropy thin-bed petrophysical evaluation processing workflow in the field-a study included the following steps: Correct resistivity value for shoulder-bed and invasion effects. Determine horizontal and vertical resistivities and Radius of Invasion (Rtv, Rth, Ri). Calculate the Resistivity Anisotropy, i.e., Aniso.=Rtv/Rth Calculate Rt reservoir and Rt shale using Rtv and Rth as inputs. Calculate water (Sw) using as inputs reservoir true resistivity (Rt sand) and shale true resistivity (Rt.sh). Comparison of Elan petrophysical evaluation with and without anisotropy. Estimate cumulative gas reserve increase due to anisotropy. RESULTS The results are shown as a comparison between a standard evaluation and a Resistivity Anisotropy analysis, over selected thinly laminated intervals encountered in the field-a. Fig. 8 and 9 present a comparison between a standard evaluation and a Resistivity Anisotropy analysis, over the thinly-bedded gasbearing interval X070-X120 m and the water SSS 3

4 bearing zone XX05-XX13 m of well X. Anisotropy was performed over water bearing zones in all the studied field-a wells for calibration purposes. The X070-X120 m interval was chosen for resistivity anisotropy analysis due to the fact that it covers the two tested zones, which produced approximately 34 MMSCF/D. This interval did not look so promising when conventional log interpretation was performed. The main reason was that over the tested zone significant contribution to production comes from thin-beds, which could not be resolved effectively by conventional logs. Fig. 8 displays the ARC and HRLA resistivities, CMR, FMI images and petrophysical evaluation results without and with Resistivity Anisotropy. The CMR transverse relaxation signal distribution (T 2 ) has a scale of 0.3 to 3000ms. A strongly bimodal (T2) signal distribution is observed over many zones within the X070-X120 m studied interval (Figure 3). This is a typical CMR response in thinly bedded intervals, because it shows the cumulative effects of shales and sands. In this zone the strongly bimodal T 2 signal is the typical signature of thinly laminated zones. Thus, this CMR characteristic response can be very beneficial for detection of thin-beds. The FMI track displays the static microconductivity borehole image; the lighter area representing the most resistive formation features. The highly laminated character of the X070-X120 m zone, as indicated by the CMR waveform is confirmed by the FMI response. Next to the FMI track, the petrophysical evaluation results are displayed ( without and with Resistivity Anisotropy) for comparison. Note the significant difference between the two techniques. The last track compares the cumulative gas volume obtained with the resistivity anisotropy method with the gas volume obtained from the conventional evaluation. A 600% increase in cumulative gas volume is obtained by the resistivity anisotropy method compared to the conventional interpretation approach. Fig. 9 shows the resistivity anisotropy results obtained over a water bearing zone of well X for calibration purposes. Note that both interpretations ( with and without resistivity anisotropy consideration) yield 100 % water. This demonstrates that the interpretation using resistivity anisotropy does not overestimate the hydrocarbon. Fig. 10 presents a comparison between the conventional petrophysical evaluation and the Resistivity Anisotropy analysis, over the thinlybedded gas-bearing interval X180-X220 m of well Y. Fig. 10 displays the AIT and HRLA resistivities, the CMR waveforms and FMI images, and the petrophysical evaluation results without and with Resistivity Anisotropy. This interval was chosen for resistivity anisotropy analysis on the basis of thin-bed response characteristics as indicated by the CMR bimodal T2 distribution and the FMI highly laminated nature images. Both tools, thus indicated the presence of many thin-beds, which can not be resolved effectively by conventional log processing and interpretation. Next to the FMI track, the petrophysical evaluation results are displayed ( without and with Resistivity Anisotropy) for comparison. Note, the significant increase of gas obtained by the resistivity anisotropy technique. The last track compares the cumulative gas volumes obtained using the resistivity anisotropy conventional evaluation methods. A 700% increase in cumulative gas volume is obtained by the resistivity anisotropy method compared to the conventional interpretation approach. Therefore, the resistivity anisotropy evaluation increases the cumulative gas reserves by more than 700% over this laminated interval. It also transforms a non-commercial and difficult to understand undepleted wet gas zone into a commercial and more petrophysically correct dry gas zone. The results of the anisotropy analysis are confirmed by the available MDT tests. CONCLUSIONS In this study, a field-scale integrated thin-bed petrophysical evaluation and hydrocarbon reserve estimation was performed, using the Resistivity Anisotropy method. All the wells in this field 4

5 intersected thinly laminated shale-silt-sand sequences and blocky sands. It is shown that in this environment using conventional resistivity data to quantify the hydrocarbon reserves is incorrect because the resistivity measurements are dominated by the conductivity of the shale laminae. The main conclusions and recommendations drawn from this study are the following: CMR and borehole image logs (FMI) are crucial for the thin-bed evaluation in the field-a. Thin beds were fairly well demarcated in all the studied field-a wells, using the Resistivity Anisotropy method. Field-A pay over studied intervals increased up to 700 % by the Resistivity Anisotropy method. These results are confirmed against a variety of independent data, including surface seismic interpretations, reservoir pressure profiles and well tests. Thin beds contain a significant amount of the total gas reserves of the field-a. Therefore, in the next wells to be drilled thin-bed analysis will be of outmost importance in enhancing formation evaluation and accurate reserve estimation. ACKNOWLEDGMENTS The authors thank Reliance Industries Limited and Schlumberger for their support during this work. REFERENCES 1. Shray, F., Borbas, T. Evaluation of lami-nated formations using nuclear magnetic resonance and resistivity anisotropy measurements, SPE 72370, Boyd, A., et al., The lowdown on low resistivity pay, Oilfield Review, Autumn Faivre, O., et al., Using array induction and array laterolog data to characterize resistivity anisotropy in vertical wells, in Transactions of the SPWLA 43rd Annual Logging Symposium, Oiso, Japan, June , paper M. 4. Yang, W., 2001, Determining Resistivity Anisotropy By Joint Lateral And Induction Logs, in Transactions of the SPWLA 42nd Annual Logging Symposium, Houston, Texas, June 17-20, Jammes, L., et al., Improved determination in thin beds environment using 2D parametric inversion, in Transactions of the 2000 SPE Technical Conference and Exhibition, Dallas, Tx, USA, October 1-4, ABOUT THE AUTHORS Kamlesh Saxena is General Manager of Exploration (Logging and Petrophysics) for Reliance Industries Limited (Oil and Gas Division). He holds a M.Tech. degree in Applied Geology from the University of Saugar, Sagar, India. He is a member of AAPG and SPE. Dr. Theodore Klimentos is a Principal Petrophysicist and Petrophysics Domain Champion with Schlumberger in India. He holds a Ph.D. degree in Rock Physics from the University of Reading, U.K. He is a member of SPWLA. SSS 5

6 Fig. 1 Definition of thin-beds: beds which are thinner than the vertical resolution of the logging device and thus cannot be resolved individually. Fig. 2 Core photo (mm scale) of laminated sand-shale-silt sequences. 6

7 Fig.3 Thin-section photos showing blocky sands (bottom), thin-beds (middle) and silts (top) from Lamcount core study of field-a. SSS Fig. 4. Two thin-bed models, i.e., one having a 10 Ohm.m resistivity sand and 1 Ohm.m resistivity shale and another model with 100 Ohm.m resistivity sand and 1 Ohm.m resistivity shale. Note that in both cases R horizontal does not exceed 2 Ohm.m. Thus, electrical current of resistivity tools is shorted by the more conductive layers, usually shale, and sensitivity to the characteristics of the more resistive reservoir layers, is lost; this results in the well known occurrence of LRLC (Low Resistivity - Low Contrast) pay. 7

8 Rhigh = 100 ohm-m, vol = 25% Rlow = 1 ohm-m, vol = 25% Rhigh = 100 ohm-m, vol = 25% Rlow = 1 ohm-m, vol = 25% R R H Rshale/silt R Rreservoir R R R V R Rshale/silt R Rreservoir Fig. 5 Bimodal resistivity sand-shale laminar model. Rv Fig. 6 Combination of induction and laterolog resistivity for resistivity anisotropy and thin-bed petrophysical evaluation. 8

9 Fig. 7 HRLA, AIT, LWD CDR, CMR and FMI recorded in a vertical A well. AIT and CDR deep (D) and shallow (S) resistivities overlay each other indicating that in the absence of AIT, LWD resistivities may be used to describe the horizontal resistivity R H. Moreover, the HRLA tool, which consists of an array of five laterologs with incremental depth of investigation, is affected by both invasion and resistivity anisotropy; the shallowest the depth of investigation of the HRLA array, the stronger its sensitivity to vertical resistivity. SSS 9

10 HRLA ARC CMR FMI Gas without anisotropy Gas with anisotropy Cumulative Gas increase Fig. 8 Well X: interval X070-X120 m-resistivity anisotropy technique for gas bearing thin beds evaluation. Comparison between resistivity anisotropy technique conventional evaluation. Resistivity Anisotropy evaluation has resulted in a 600% increase of cumulative gas volume. 10

11 HRLA ARC CMR FMI Fluid without anisotropy Fluid with anisotropy SSS Fig. 9 Well X: water bearing interval XX05-XX13m - Resistivity anisotropy technique for calibration over water bearing zone. Comparison between resistivity anisotropy technique conventional evaluation. Resistivity Anisotropy evaluation shows water. 11

12 HRLA AIT CMR FMI Gas without anisotropy Gas with anisotropy Cumulative Gas increase Fig. 10 Well Y: interval X180-X220 m - Resistivity anisotropy technique for gas bearing thin beds evaluation. Comparison between resistivity anisotropy technique conventional evaluation. Resistivity Anisotropy evaluation has resulted in a 700% increase of cumulative gas volume. 12