In order to enhance the development efficiency of thick and complex carbonate reservoirs in the Middle East, a case study was conducted on M oilfield in Iraq. This study focused on reservoir characterization, injection-production modes, well pattern optimization, and other related topics. As a result, key techniques for the high-efficiency development of thick carbonate reservoirs were established. The research findings include the following: (1) the discovery of hidden “low-velocity” features within the thick gypsum-salt layer, which led to the development of a new seismic velocity model; (2) the differential dissolution of grain-supported limestones is controlled by lithofacies and petrophysical properties, resulting in the occurrence of “porphyritic” phenomena in core sections. The genetic mechanism responsible for reversing petrophysical properties in dolostones is attributed to “big hole filling and small hole preservation” caused by dense brine refluxing; (3) fracture evaluation technology based on anisotropy and dipole shear wave long-distance imaging was developed to address challenges associated with quantitatively assessing micro-fractures; (4) through large-scale three-dimensional physical models and numerical simulations, it was revealed that water–oil displacement mechanisms involving “horizontal breakthrough via hyper-permeability” combined with vertical differentiation due to gravity occur in thick and heterogeneous reservoirs under spatial injection-production modes; (5) a relationship model linking economic profit with well pattern density was established for technical service contracts in the Middle East. Additionally, an innovative stepwise conversion composite well patterns approach was introduced for thick reservoirs to meet production ramp-up requirements while delaying water cut rise; (6) a prediction technology for the oilfield development index, considering asphaltene precipitation, has been successfully developed. These research findings provide robust support for the efficient development of the M oilfield in Iraq, while also serving as a valuable reference for similar reservoirs’ development in the Middle East.

Select porous carbonate cores are used to carry out water-flooding oil micro-CT flooding experiments, and use image processing to separate oil, water, microfacies, and rock skeleton. The gray value is used to determine the distribution position of the microfacies sub-resolution remaining oil. The gray image resolution is improved by the SRCNN method to improve the pore identification accuracy. The distribution and evolution law of the sub-resolution remaining oil after the displacement is determined by the oil-water distribution results. Using the SRCNN method, the pore recognition accuracy of the original scanned images of the two samples was increased by 47.88 times and 9.09 times, respectively. The sub-resolution residual oil and the macro-pore residual oil were determined from the CT scan images after the brine was saturated and divided into five categories. With the increase in the displacement ratio, the columnar and droplet residual oil of the low-permeability samples first increased and then decreased, and the cluster residual oil gradually decreased. The continuous residual oil of the hypertonic samples gradually decreased, and the discontinuous residual oil gradually increased. According to the research results of carbonate pore throat identification and sub-resolution microscopic residual oil change characteristics after water flooding under the SRCNN method, a method for distinguishing porous carbonate reservoirs is provided.

Fractured-porous carbonate reservoirs, mainly composed of dolomites and crystalline rocks with various rock types and extremely poor initial porosity and permeability, are dominated by tectonic fractures and exhibit extreme heterogeneity. The fracture system plays a predominant role in hydrocarbon fluid transport. Compared with conventional sandstone reservoirs, fracture geometry and topological structure parameters are key factors for the accuracy and computational efficiency of numerical simulation history matching in fractured reservoirs. To address the matching issue, this paper introduces an artificial intelligence history matching method combining the Monte Carlo experimental planning method with an artificial neural network and a particle swarm optimization algorithm. Taking reservoir geological parameters and phase infiltration properties as the objective function, this method performs reservoir production history matching to correct the geological model. Through case studies, it is verified that this method can accurately correct the geological model of fractured-porous reservoirs and match the observed production data. This research represents a collaborative effort among multiple disciplines, integrating advanced algorithms and geological knowledge with the expertise of computer scientists, geologists, and engineers. Currently the world’s major oilfields history fitting is mainly based on reservoir engineers’ experience to fit; the method is applicable to major oilfields, but the fitting accuracy and fitting efficiency is severely limited, the fitting accuracy is less than 75%, while the artificial intelligence history fitting method shows a stronger applicability; intelligent history fitting is mainly based on the integrity of the field data, and as far as the theory is concerned, the accuracy of the intelligent history fitting can be up to 100%. Therefore, AI history fitting can provide a significant foundation for mine field research. Future research could further explore interdisciplinary collaboration to address other challenges in reservoir characterization and management.