Coalbed methane (CBM) reservoir performance is controlled by a complex set of reservoir, geologic, completion and operation parameters and the inter-relationships between those parameters. In order to identify, analyze and mitigate risks associated with any CBM prospect, one must first understand the relative importance of each of these parameters, how their relative importance changes under different constraints, and how they interactively affect CBM production. To date, no comprehensive parametric study on coalbed methane is known to have been conducted within the industry. The parametric studies that do exist in the literature have only considered a limited set of parameters and a limited range for those parameters while holding all the other parameters fixed, thus potentially skewing their results. This comprehensive parametric study has been conducted to enhance our understanding of CBM reservoir performance. In essence, it is an analysis of an extensive Monte Carlo simulation of more than 100,000 reservoir simulations in which the reservoir, geologic, completion and operational variables that impact the production performance of CBM reservoirs were varied. While it is stated that this was a coalbed methane parametric study, coalbed methane reservoirs typically occur in conjunction with sandstones of varying quality. For that reason, one-half of the reservoir simulations modeled the production from coalbed methane reservoirs in conjunction with sandstones of varying quality and degrees of wetness (fully water charged to various degrees of gas-charge). The study's goal was to establish the relative importance of the identified parameters and their inter-relationships, developing rank correlations between these parameters against several production end states (e.g., peak gas rates, dewatering times, cumulative gas, etc.). To avoid some of the potential problems of other similar studies, this study aimed to be as comprehensive as possible in the coverage of parameters varied with the tools available. In so doing, it provides a fuller understanding of the parameters involved in coalbed methane production and enables uncertainties and risk to be more accurately assessed for project economics, and will ultimately lead to the development of more accurate and efficient CBM screening models. Background Coalbed Methane is truly an unconventional gas resource. The most obvious difference between CBM and conventional gas reservoirs is in the gas storage mechanism. In conventional gas reservoirs, gas is stored as free gas in the pore spaces of the reservoir rock. In CBM reservoirs, the gas may be stored both as a free gas in the secondary porosity network (cleats or natural fractures) or the gas may be stored at almost liquid densities on the surface matrix of the coal by physical sorption. Sorption commonly accounts for over 99% of the gas-in-place in CBM reservoirs. To produce gas from a CBM reservoir, gas must first be desorbed from the coal. This is accomplished in practice by depressurizing the coals. Since most CBM reservoirs are 100% water saturated in the natural cleat fracture system, this requires water to be produced to depressurize the coal. Once gas has desorbed from the coal matrix, it diffuses through the coal matrix to the natural fracture network which provides the primary conduit to the production well.
An integrated asset modeling (IAM) approach has been implemented for the Alpine Field and eight associated satellite fields on the Western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir and facilities management options under existing and future operating constraints. Oil, gas and water production from these fields are processed at the Alpine Central Facility (ACF). A number of local constraints exist for the asset, such as the requirement that all associated gas be used for facilities power generation, gas lift or re-injection. All produced water must be re-injected and, for pipeline integrity reasons, must be segregated from imported make-up sea water used for injection. Additionally, surface gas and water handling capacity is limited at the ACF. To further complicate matters, gas injected for EOR purposes is enriched such that it is miscible or near-miscible at reservoir conditions. These conditions create a unique and changing relationship between the oil, gas and water production, gas lift, miscible water alternating gas (MWAG) injection, lean gas injection, facilities constraints and injection availability.The IAM technology utilized for managing the WNS fields consists of full-field compositional reservoir simulation models for each reservoir integrated with a pipeline surface network model and a process facility model. Spreadsheet based allocation routines and advanced mathematical coupling algorithms complete the IAM model enabling not only the prediction of the assets' performance under the aforementioned constraints, capacities and operating conditions, but to optimize overall performance and analyze the impact of decisions. To the authors' knowledge, this is the first time integrated asset modeling has been applied to bring the entire production stream including reservoir, wellbore, surface network and process simulation together for planning and managing MWAG injection to optimize recovery from an existing development.
Four distinct sequential phases form a recommended process for coalbed-methane (CBM)-prospect assessment: initial screening, reconnaissance, pilot testing, and final appraisal. Stepping through these four phases provides a program of progressively ramping work and cost, while creating a series of discrete decision points at which analysis of results and risks can be assessed. While discussing each of these phases in some degree, this paper focuses on the third, the critically important pilot-testing phase.This phased CBM-prospect assessment process allows us to • Gain local knowledge early at low cost • Progressively acquire and compile appropriate data to assess the geological situation and reservoir conditions systematically • Identify and attempt to fill the most important knowledge gaps that represent the greatest uncertainties and risks to the prospect • Increasingly understand the distributions of key parameters that control reserves, deliverability, and value• Stage expenditures and provide multiple decision points through the process • Ultimately, produce a project with very low development risk In the CBM-prospect assessment process, the pilot test serves the same function as a conventional exploration well. If it proves successful, then the prospect can be considered a discovery and can be appraised for development. By drilling, completing, and producing a cluster of wells in a CBM pilot test, short of proceeding directly to a partial development, we are able to locally dewater and depressurize the coal seam to be tested and, thereby, desorb and deliver measurable volumes of gas. If correctly implemented, the pilot test allows us to assess the local variability of key reservoir parameters, collect the information necessary to simulate the reservoir's producibility, and, thereby, estimate potential project reserves to a reasonable degree of accuracy. This paper contains roughly 30 specific recommendations and the fundamental rationale behind each recommendation to help ensure that a CBM pilot will fulfill its primary objectives of (1) demonstrating whether the subject coal reservoir will desorb and produce consequential gas and (2) gathering the data critical to evaluate and risk the prospect at the next-often most critical-decision point. Importantly, these objectives must be met in a timely manner. To do this, the specifications for the pilot are often not those that will be used for an optimized well or field-development pattern in terms of costs or production. This is intentional. The goals of piloting are different from the goals of development. So, the recommended designs are different. The pilot design recommendations focus on collecting superior data that will quantify key parameters for interpretation and simulation of the reservoir, retaining flexibility in the face of the level of uncertainty remaining after the reconnaissance phase, and arriving at a definitive answer on the coal reservoir's viability in an acceptable time frame.Detailed data-analysis methods for CBM are not discussed here-th...
This paper was seleciad for pmaenta!lon by an SPE ProgramCommmee followlng review of inlormaOon contained in an abstracf aubmmed by the author(s). contents of the paper, as presented, have not been reviewed by the .%ciity of Petro!-aum Engineers and are subject to correction by the author(s). The materiil, as prasanted. doesnot necessarily reflect any position of the Sc@aty 01 Petroleum Engineers, ils otfiim, or membem. Papers presented al SPE meetings are subject to publication review by Edttorlal CommHiees of the Seciety of Petroleum Engkmers. Permisa!a to ccpy is restricted to an abstrect 01 not mofe than 300 words. llluWal$OrM may not be COpiOd The abstract should contain ccnmpkxmus acknolwedgment of where and by whom the papw waa presented. Write Librarian, SPE, PO E&x 833836, Rchardaon, TX 750S3-3636, LISA., lax 01-214-952-9435.The theoretical results presented in this work are generally applicable to both single-phase and multiphase reservoirs which are either infinite-acting or contain a constant pressure boundary. The results are particularly useful for analyzing the pressure response of wells producing reservoirs undergoing an immiscible displacement process, either natural or induced.
Variograms for permeability are typically calculated using well log and core measurements, if they are calculated at all. Such procedures are inadequate to estimate variograms for horizontal permeability as they yield almost no information about the short lag structure of the variogram. A well test samples a much larger region of the reservoir than log and core measurements and thus, well test responses are potential sources of data for estimating the horizontal permeability variogram In Ref. 1, we established the feasibility of calculating this variogram from a single set of well test data. Here we summarize further research on estimating horizontal permeability variograms from well test data. Our major focus in this paper is on improving horizontal variogram estimates by combining responses from tests on a number of different wells. We also address a few issues related to variogram estimation from single well test data. All work presented here is based on simulated well test data using permeability distributions generated with an imposed correlation structure and level of heterogeneity. Introduction In Ref. 1, we established the feasibility of calculating horizontal permeability variograms from single well test data. We also indicated some of the limitations for estimating variograms from single test data. If the region sampled is representative of the overall permeability distribution, then single well test data can be used to successfully estimate a normalized or scaled variogram. Even when the correlation length can be estimated successfully from the well test derived variogram, the shape of the variogram at short lags is often distorted or spurious. The nature of the sampled region governs the shape of the variogram. Selecting a region of the reservoir a priori, that reflects the nature of the overall permeability distribution is impossible. A logical approach for overcoming the difficulties outlined above is to estimate the permeability variogram by combining in some way data from multiple single well tests. It appears intuitive that by combining data influenced by different regions of the reservoir, variograms estimated from multiple single well tests are more likely to be representative of the true variogram than that derived from a single test. The major goal of this work is to test this idea for improving variogram estimates. We also address a number of issues raised by our earlier study. In particular, sensitivity of the well test derived variogram to time step grids and spatial grid sizes is examined. All results presented in this paper are based on simulated well test data. Every permeability realization is constrained to possess a log-normal histogram and an isotropic variogram All correlated realizations have a spherical variogram structure. Permeability realizations are generated using either simulated annealing or sequential gaussian algorithms. Drawdown well test responses are simulated for these realizations using a three dimensional, semi-implicit, multi- well, finite-difference simulator which was developed by Amoco for simulating pressure transient responses of complex, systems and for modeling full-field multi-well gas reservoirs. P. 597
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