Determination of gas in place (GIP) is among the hotspot issues in the field of oil/gas reservoir engineering. The conventional material balance method and other relevant approaches have found widespread application in estimating GIP of a gas reservoir or well-controlled gas reserves, but they are normally not cost-effective. To calculate GIP of abnormally pressured gas reservoirs economically and accurately, this paper deduces an iteration method for GIP estimation from production data, taking into consideration the pore shrinkage of reservoir rock and the volume expansion of irreducible water, and presents a strategy for selecting an initial iteration value of GIP. The approach, termed DMBM-APGR (dynamic material balance method for abnormally pressured gas reservoirs) here, is based on two equations: dynamic material balance equation and static material balance equation for overpressured gas reservoirs. The former delineates the relationship between the quasipressure at bottomhole pressure and the one at average reservoir pressure, and the latter reflects the relationship between average reservoir pressure and cumulative gas production, both of which are rigidly demonstrated in the paper using the basic theory of gas flow through porous media and material balance principle. The method proves effective with several numerical cases under various production schedules and a field case under a variable rate/variable pressure schedule, and the calculation error of GIP does not go beyond 5% provided that the production data are credible. DMBM-APGR goes for gas reservoirs with abnormally high pressure as well as those with normal pressure in virtue of its strict theoretical foundation, which not only considers the compressibilities of rock and bound water, but also reckons with the changes in production rate and variations of gas properties as functions of pressure. The method may serve as a valuable and reliable tool in determining gas reserves.

Low permeability porous carbonate rocks occupy a certain proportion in the Middle East. Horizontal injection-production well pattern development is often adopted. Due to the influence of well type and wellbore, reservoir dynamic monitoring is mainly based on conventional daily measurement data, well test and pressure monitoring. Therefore, it is particularly important to combine well test interpretation with production dynamic analysis to diagnose the main control factors and production characteristics of this type of reservoir. In this paper, the point source function is used to obtain the pressure variation function of a horizontal well in infinite formation with upper and lower closed boundary. The difference between the horizontal well test curve of A reservoir and the typical horizontal well test curve is compared and analyzed, and the abnormal well test curve of horizontal wells is characterized by a linear flow phase with a slope of 1/3 or 1/4. The abnormal well test curve accounted for 33.34%. The main influencing factors are the permeability around the well and the well trajectory. By combining well test interpretation with dynamic inversion method, the correlation between well test interpretation and dynamic characteristics of horizontal wells with different characteristics is classified and clarified. The main controlling factors that affect the difference in the water injection development effect of different horizontal wells are further clarified, and provide an important reference for the adjustment of injection-production parameters and the optimal deployment of schemes.

The estimation of reserves and performance prediction are two vital tasks for the development of gas reservoirs where the evaluation of gas in place or well-controlled reserves, as the foundation of the performance analysis of gas wells, turns to be exceedingly significant. Advanced production data analysis or modern rate transient analysis (RTA) methods mainly depend on the iterative calculations of material balance quasitime () and type curve fitting, the essence of which is to update the average reservoir pressure data time and again. The traditional Arps’ decline models are of empirical nature despite the convenience and applicability to the constant bottomhole pressure (BHP) condition. In order to avoid the implicit iteration, this paper develops an explicit method for estimating the average reservoir pressure on the basis of dynamic material balance equation (DMBE), termed “flow integral method,” which can be applied to various gas production systems under boundary-dominated flow (BDF). Based on the flow integral method and the decline parameter evaluation, we employ the hyperbolic decline model to model the gas well performance at a constant BHP. The analytical formulations of decline rate and decline exponent are deduced from the DMBE and the static material balance equation (SMBE) considering the elastic compressibilities of rock pore and bound water. The resulting decline parameter method for explicit estimation of gas reserves boasts a solid and rigorous theory foundation that production rate, decline rate, and average reservoir pressure profiles have reference to each other, and its implementation steps are explained in the paper. The SMBE can, combined with the estimated pressure profile by the flow integral method, also be used to determine gas reserves which is not limited to the constant-BHP condition and can calibrate the estimates of the decline parameter method. The proposed methods are proven effective and reliable with several numerical cases at different BHPs and a field example.

Waterflooding, the predominant secondary recovery method in global sandstone and carbonate reservoirs, faces challenges including premature water breakthroughs, rapid water cut rise, limited well pattern adjustments and restricted stimulation treatments due to complex geological constraints. This demands enhanced optimization techniques. Leveraging streamline simulation’s flow diagnostic capabilities, this study introduces two novel metrics: “real-time streamline revenue” (RTSR), quantifying the economic effectiveness via flux, time of flight and saturation data integration along streamlines, and “well-pair revenue efficiency” for injection-production unit characterization. Integrating corresponding rate optimization criteria, we develop an RTSR-based production optimization methodology which enables rapid generation of optimal injection-production schedules, improving recovery while controlling water production. Validation using synthetic and field-scale models (Reservoir M) demonstrated significant improvements by the proposed method: Synthetic case achieved 29.42% NPV increase, 26.88% oil production rise, and 8.60% water reduction compared to the base schedule; Reservoir M yielded 20.80% higher NPV, 20.41% more oil, and 72.69% less water. The approach outperforms existing streamline methods, proving effective for stabilizing/enhancing oil production and reducing water cut. Future work can refine weighting functions within optimization criteria using surrogate-optimization algorithms and extend the framework to integrate layer series, well patterns, or well placement with injection-production control.